import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime
from datetime import date
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

# Read MA PPE data
df = pd.read_csv('MA_ppe_regions.csv', parse_dates=['Time'])
df.drop(['Total Persons Tested','Percent Positivity','Deaths','Hospitalized Total', 'Hospitalized ICU'], axis=1, inplace=True)
df = df.loc[df['Region']!='State Agency'].dropna(how='all')
df

Note:¶

This notebook is generalized in three parts for each region to analyze PPE distribution vs confirmed cases.¶

1: Figures of absolue value of cumulative PPE data vs cumulative confirmed cases data.¶

2: Figures of ratios of PPE data to confirmed cases. (See comments for 6 different ratios)¶

3: Figures of regression and results.¶

Summary¶

1: This data set is from DPH and MEMA. The PPEs are distributed to those who will have a supply shortage within the next 5 days and are not able to sourcethe material through their normal supply chain. This data set may not show the entire pucture of PPE supply in Mass.¶

2: At first, Feds gave Mass. the least amount of PPE but sent large shipments to states with smaller population and far few cases. (https://www.wbur.org/news/2020/07/23/fema-masks-ppe-massachusetts-coronavirus)¶

3: There is a huge increase on July 22 for N95s/KN95s which may be caused by large supplys from FEMA.(Not sure)¶

4: For the same ppe, it has similar distribution trend in different regions while different ppes have different trends in the same region.¶

5: There is a linear relationship between cumulative gowns/gloves and cumulative confirmed cases based on the data we have. If we ignore the data from 7/22/2020, the relationship also exists between cumulative N95s/KN95s/Masks and cumulative confirmed cases.¶

...

PPE Distribution vs Confirmed Cases¶

Southeastern Massachusetts¶

SM = df.loc[df['Region']=='Southeastern Massachusetts']
SM_CC = SM.dropna()
categories = ['N95s/KN95s', 'Masks', 'Gowns', 'Gloves', 'Ventilators']
for category in categories:
    SM_CC['{} %'.format(category)] = SM_CC[category]/SM_CC[category].iloc[-1] * 100
    
fig, axes = plt.subplots(2, 3, figsize=(25,15))
for ax, category in zip(axes.flatten(), categories):
    sns.distplot(SM_CC[category].diff(), ax=ax)
    ax.set_xlabel(category, fontsize=15)
fig.delaxes(axes[1,2])
SM_CC

Distplot of PPE Growth

# Confirmed cases vs PPE distribution
fig, axes = plt.subplots(2,1,figsize=(15,14))

percentages = ['N95s/KN95s %', 'Masks %', 'Gowns %', 'Gloves %', 'Ventilators %']

for category in categories:
    axes[0].plot('Time', category, data=SM_CC, marker='.', markersize=10)

axes[0].set_xlabel('Date', fontsize=15)
axes[0].set_ylabel('The Number of PPE Distribution', fontsize=15)
axes[0].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020, 9,9)])
axes[0].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[0].legend()
axes[0].set_title('PPE Distribution vs Confirmed Cases in SM', fontsize=20)

ax1 = axes[0].twinx()
ax1.plot('Time', 'Confirmed Cases', data=SM_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax1.set_ylabel('Confirmed Cases', fontsize=15)
ax1.tick_params(direction='out', length=6, width=2, labelsize=10)
ax1.legend(loc='upper right')


for percentage in percentages:
    axes[1].plot('Time', percentage, data=SM_CC, marker='.', markersize=10)

axes[1].set_xlabel('Date', fontsize=15)
axes[1].set_ylabel('PPE Distribution %', fontsize=15)
axes[1].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020, 9,9)])
axes[1].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[1].legend()
axes[1].set_title('PPE Distribution Percentage vs Confirmed Cases in SM', fontsize=20)

ax2 = axes[1].twinx()
ax2.plot('Time', 'Confirmed Cases', data=SM_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax2.set_ylabel('Confirmed Cases', fontsize=15)
ax2.tick_params(direction='out', length=6, width=2, labelsize=10)
ax2.legend(loc='upper right')

plt.show()

pd.set_option("display.max_rows", None, "display.max_columns", None)


# Calculate the ratio of cumulative PPE data to cumulative confirmed cases data (Ratio 1)
for category in categories:
    SM_CC['{} Ratio 1'.format(category)] = SM_CC[category]/SM_CC['Confirmed Cases']
    
    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 2)
# (Interval=2 weeks, one week before + the week itself)
SM_CC['Confirmed Cases(ma1)'] = SM_CC['Confirmed Cases'].rolling(window=2,center=False).mean()
SM_CC.iloc[0,-1] = (6065 + 9446) / 2
for category in categories:
    SM_CC['{} Ratio 2'.format(category)] = SM_CC[category]/SM_CC['Confirmed Cases(ma1)']





# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 3)
# (Interval=2 weeks, one week after + the week itself)
SM_CC['Confirmed Cases(ma2)'] = SM_CC['Confirmed Cases'].rolling(window=2,center=False).mean().shift(-1)
for category in categories:
    SM_CC['{} Ratio 3'.format(category)] = SM_CC[category]/SM_CC['Confirmed Cases(ma2)']

    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 4)
# (Interval=3 weeks, one week before + one week after + the week itself)
SM_CC['Confirmed Cases(ma3)'] = SM_CC['Confirmed Cases'].rolling(window=3,center=False).mean().shift(-1)
SM_CC.iloc[0,-1] = (6065+9446+11616)/3
for category in categories:
    SM_CC['{} Ratio 4'.format(category)] = SM_CC[category]/SM_CC['Confirmed Cases(ma3)']

    
    
# Calculate the ratio of delta PPE data to delta confirmed cases data (Ratio 5)
for category in categories:
    SM_CC['{} Ratio 5'.format(category)] = SM_CC[category].diff()/SM_CC['Confirmed Cases'].diff()
    
    
    
# Calculate the ratio of delta PPE data to moving average delta confirmed cases data (Ratio 6)
for category in categories:
    SM_CC['{} Ratio 6'.format(category)] = SM_CC[category].diff()/SM_CC['Confirmed Cases'].diff().rolling(window=2,center=False).mean()
# SM_CC

# Plot the Ratio of PPE to Confirmed Cases

RATIO1 = []
RATIO2 = []
RATIO3 = []
RATIO4 = []
RATIO5 = []
RATIO6 = []
RATIOS = [RATIO1, RATIO2, RATIO3, RATIO4, RATIO5, RATIO6]
for RATIO in RATIOS:
    for category in categories:
        RATIO.append('{} Ratio {}'.format(category, RATIOS.index(RATIO)+1))

fig, ax = plt.subplots(1,1,figsize=(14, 6))
color = ['b', 'g', 'r', 'c', 'm']

for category in categories:
    ax.plot('Time', category, data=SM_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
    ax.set_xlabel('Date', fontsize=15)
    ax.set_ylabel('The number of PPE', fontsize=15)
    ax.set_xlim([datetime.date(2020, 4, 29), datetime.date(2020, 9,9)])
    ax.legend(loc='best')
    ax.set_title('The Ratio of PPE Dstribution to Confirmed Cases in Southeastern Massachusetts', fontsize=20)
ax1 = ax.twinx()
for ratio in RATIO1:
    ax1.plot('Time', ratio, data=SM_CC, color=color[RATIO1.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
    ax1.set_ylabel('Ratio', fontsize=15)
    ax1.legend(loc='best')
plt.show()

         
         
fig, axes = plt.subplots(6,1,figsize=(15,48))
color = ['b', 'g', 'r', 'c', 'm']

for RATIO in RATIOS:
    for category in categories:
        axes[RATIOS.index(RATIO)].plot('Time', category, data=SM_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
        axes[RATIOS.index(RATIO)].set_xlabel('Date', fontsize=15)
        axes[RATIOS.index(RATIO)].set_ylabel('The number of PPE', fontsize=15)
        axes[RATIOS.index(RATIO)].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
        axes[RATIOS.index(RATIO)].legend(loc='best')
        axes[RATIOS.index(RATIO)].set_title('The Ratio of PPE Dstribution to Confirmed Cases in Southeastern Massachusetts', fontsize=20)
    ax1 = axes[RATIOS.index(RATIO)].twinx()
    for ratio in RATIO:
        ax1.plot('Time', ratio, data=SM_CC, color=color[RATIO.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
        ax1.set_ylabel('Ratio', fontsize=15)
        ax1.legend(loc='best')
plt.show()

for category in categories:
    SM_CC['{} delta'.format(category)] = SM_CC[category].diff()
    SM_CC['Confirmed Cases delta'] = SM_CC['Confirmed Cases'].diff()

fig, axes = plt.subplots(2, 3, figsize=(29,15))

for ax, category in zip(axes.flatten(), categories):
    sns.regplot('Confirmed Cases', category, data=SM_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=20)
    ax.set_ylabel(category, fontsize=20)
    ax.tick_params(direction='out', length=6, width=2, labelsize=12)
fig.delaxes(axes[1,2])
fig.suptitle('Regression Plot for Southeastern Massachusetts(Cumulative vs Cumulative)', fontsize=30)

fig1, axes1 = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes1.flatten(), categories):
    sns.regplot('Confirmed Cases delta', category + ' delta', data=SM_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig1.delaxes(axes1[1,2])
fig1.suptitle('Regression Plot(delta vs delta)', fontsize=25)

Text(0.5, 0.98, 'Regression Plot(delta vs delta)')

# Regression Analysis
import statsmodels.api as sm

Y = [pd.DataFrame] * 5
X = [pd.DataFrame] * 5
for category in categories:
    Y[categories.index(category)] = SM_CC[category]
    X[categories.index(category)] = SM_CC['Confirmed Cases']
    X[categories.index(category)] = sm.add_constant(X[categories.index(category)])
    model = sm.OLS(Y[categories.index(category)], X[categories.index(category)])
    results = model.fit()
    print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:             N95s/KN95s   R-squared:                       0.507
Model:                            OLS   Adj. R-squared:                  0.480
Method:                 Least Squares   F-statistic:                     18.51
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           0.000429
Time:                        20:58:00   Log-Likelihood:                -280.81
No. Observations:                  20   AIC:                             565.6
Df Residuals:                      18   BIC:                             567.6
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.186e+06   4.36e+05     -2.718      0.014    -2.1e+06   -2.69e+05
Confirmed Cases   107.1923     24.913      4.303      0.000      54.851     159.533
==============================================================================
Omnibus:                        9.838   Durbin-Watson:                   0.150
Prob(Omnibus):                  0.007   Jarque-Bera (JB):                2.154
Skew:                           0.214   Prob(JB):                        0.341
Kurtosis:                       1.450   Cond. No.                     1.07e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.07e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Masks   R-squared:                       0.564
Model:                            OLS   Adj. R-squared:                  0.540
Method:                 Least Squares   F-statistic:                     23.30
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           0.000135
Time:                        20:58:00   Log-Likelihood:                -283.86
No. Observations:                  20   AIC:                             571.7
Df Residuals:                      18   BIC:                             573.7
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.374e+06   5.08e+05     -2.705      0.015   -2.44e+06   -3.07e+05
Confirmed Cases   139.9880     29.004      4.827      0.000      79.053     200.923
==============================================================================
Omnibus:                       10.775   Durbin-Watson:                   0.146
Prob(Omnibus):                  0.005   Jarque-Bera (JB):                2.120
Skew:                           0.100   Prob(JB):                        0.346
Kurtosis:                       1.418   Cond. No.                     1.07e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.07e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Gowns   R-squared:                       0.983
Model:                            OLS   Adj. R-squared:                  0.982
Method:                 Least Squares   F-statistic:                     1024.
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           2.57e-17
Time:                        20:58:00   Log-Likelihood:                -190.91
No. Observations:                  20   AIC:                             385.8
Df Residuals:                      18   BIC:                             387.8
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -2.182e+04   4870.244     -4.480      0.000   -3.21e+04   -1.16e+04
Confirmed Cases     8.8974      0.278     31.995      0.000       8.313       9.482
==============================================================================
Omnibus:                       10.626   Durbin-Watson:                   1.050
Prob(Omnibus):                  0.005   Jarque-Bera (JB):                8.090
Skew:                          -1.269   Prob(JB):                       0.0175
Kurtosis:                       4.809   Cond. No.                     1.07e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.07e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Gloves   R-squared:                       0.986
Model:                            OLS   Adj. R-squared:                  0.985
Method:                 Least Squares   F-statistic:                     1224.
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           5.28e-18
Time:                        20:58:00   Log-Likelihood:                -236.99
No. Observations:                  20   AIC:                             478.0
Df Residuals:                      18   BIC:                             480.0
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -2.868e+05   4.88e+04     -5.882      0.000   -3.89e+05   -1.84e+05
Confirmed Cases    97.3975      2.784     34.982      0.000      91.548     103.247
==============================================================================
Omnibus:                        1.993   Durbin-Watson:                   0.861
Prob(Omnibus):                  0.369   Jarque-Bera (JB):                1.440
Skew:                          -0.643   Prob(JB):                        0.487
Kurtosis:                       2.724   Cond. No.                     1.07e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.07e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:            Ventilators   R-squared:                        -inf
Model:                            OLS   Adj. R-squared:                   -inf
Method:                 Least Squares   F-statistic:                    -18.00
Date:                Sun, 13 Sep 2020   Prob (F-statistic):               1.00
Time:                        20:58:00   Log-Likelihood:                 612.88
No. Observations:                  20   AIC:                            -1222.
Df Residuals:                      18   BIC:                            -1220.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const              79.0000   1.71e-14   4.62e+15      0.000      79.000      79.000
Confirmed Cases  3.469e-18   9.77e-19      3.550      0.002    1.42e-18    5.52e-18
==============================================================================
Omnibus:                        5.214   Durbin-Watson:                   0.214
Prob(Omnibus):                  0.074   Jarque-Bera (JB):                3.191
Skew:                           0.940   Prob(JB):                        0.203
Kurtosis:                       3.543   Cond. No.                     1.07e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.07e+05. This might indicate that there are
strong multicollinearity or other numerical problems.

Northeastern Massachusetts¶

Northeastern Massachusetts is the region with the most confirmed cases number.¶

NM = df.loc[df['Region']=='Northeastern Massachusetts']
NM_CC = NM.dropna()
categories = ['N95s/KN95s', 'Masks', 'Gowns', 'Gloves', 'Ventilators']
for category in categories:
    NM_CC['{} %'.format(category)] = NM_CC[category]/NM_CC[category].iloc[-1] * 100
    
fig, axes = plt.subplots(2, 3, figsize=(25,15))
for ax, category in zip(axes.flatten(), categories):
    sns.distplot(NM_CC[category].diff(), ax=ax)
    ax.set_xlabel(category, fontsize=15)
fig.delaxes(axes[1,2])
NM_CC

# Confirmed cases vs PPE distribution
fig, axes = plt.subplots(2,1,figsize=(15,14))

percentages = ['N95s/KN95s %', 'Masks %', 'Gowns %', 'Gloves %', 'Ventilators %']

for category in categories:
    axes[0].plot('Time', category, data=NM_CC, marker='.', markersize=10)

axes[0].set_xlabel('Date', fontsize=15)
axes[0].set_ylabel('The Number of PPE Distribution', fontsize=15)
axes[0].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[0].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[0].legend()
axes[0].set_title('PPE Distribution vs Confirmed Cases in NM', fontsize=20)

ax1 = axes[0].twinx()
ax1.plot('Time', 'Confirmed Cases', data=NM_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax1.set_ylabel('Confirmed Cases', fontsize=15)
ax1.tick_params(direction='out', length=6, width=2, labelsize=10)
ax1.legend(loc='upper right')


for percentage in percentages:
    axes[1].plot('Time', percentage, data=NM_CC, marker='.', markersize=10)

axes[1].set_xlabel('Date', fontsize=15)
axes[1].set_ylabel('PPE Distribution %', fontsize=15)
axes[1].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[1].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[1].legend()
axes[1].set_title('PPE Distribution Percentage vs Confirmed Cases in NM', fontsize=20)

ax2 = axes[1].twinx()
ax2.plot('Time', 'Confirmed Cases', data=NM_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax2.set_ylabel('Confirmed Cases', fontsize=15)
ax2.tick_params(direction='out', length=6, width=2, labelsize=10)
ax2.legend(loc='upper right')

plt.show()

pd.set_option("display.max_rows", None, "display.max_columns", None)


# Calculate the ratio of cumulative PPE data to cumulative confirmed cases data (Ratio 1)
for category in categories:
    NM_CC['{} Ratio 1'.format(category)] = NM_CC[category]/NM_CC['Confirmed Cases']
    
    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 2)
# (Interval=2 weeks, one week before + the week itself)
NM_CC['Confirmed Cases(ma1)'] = NM_CC['Confirmed Cases'].rolling(window=2,center=False).mean()
NM_CC.iloc[0,-1] = (10641+14920) / 2
for category in categories:
    NM_CC['{} Ratio 2'.format(category)] = NM_CC[category]/NM_CC['Confirmed Cases(ma1)']





# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 3)
# (Interval=2 weeks, one week after + the week itself)
NM_CC['Confirmed Cases(ma2)'] = NM_CC['Confirmed Cases'].rolling(window=2,center=False).mean().shift(-1)
for category in categories:
    NM_CC['{} Ratio 3'.format(category)] = NM_CC[category]/NM_CC['Confirmed Cases(ma2)']

    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 4)
# (Interval=3 weeks, one week before + one week after + the week itself)
NM_CC['Confirmed Cases(ma3)'] = NM_CC['Confirmed Cases'].rolling(window=3,center=False).mean().shift(-1)
NM_CC.iloc[0,-1] = (10641+14920+18056)/3
for category in categories:
    NM_CC['{} Ratio 4'.format(category)] = NM_CC[category]/NM_CC['Confirmed Cases(ma3)']

    
    
# Calculate the ratio of delta PPE data to delta confirmed cases data (Ratio 5)
for category in categories:
    NM_CC['{} Ratio 5'.format(category)] = NM_CC[category].diff()/NM_CC['Confirmed Cases'].diff()
    
    
    
# Calculate the ratio of delta PPE data to moving average delta confirmed cases data (Ratio 6)
for category in categories:
    NM_CC['{} Ratio 6'.format(category)] = NM_CC[category].diff()/NM_CC['Confirmed Cases'].diff().rolling(window=2,center=False).mean()
NM_CC

# Plot the Ratio of PPE to Confirmed Cases

RATIO1 = []
RATIO2 = []
RATIO3 = []
RATIO4 = []
RATIO5 = []
RATIO6 = []
RATIOS = [RATIO1, RATIO2, RATIO3, RATIO4, RATIO5, RATIO6]
for RATIO in RATIOS:
    for category in categories:
        RATIO.append('{} Ratio {}'.format(category, RATIOS.index(RATIO)+1))
        
fig, ax = plt.subplots(1,1,figsize=(14, 6))
color = ['b', 'g', 'r', 'c', 'm']

for category in categories:
    ax.plot('Time', category, data=NM_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
    ax.set_xlabel('Date', fontsize=15)
    ax.set_ylabel('The number of PPE', fontsize=15)
    ax.set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
    ax.legend(loc='best')
    ax.set_title('The Ratio of PPE Dstribution to Confirmed Cases in Northeastern Massachusetts', fontsize=20)
ax1 = ax.twinx()
for ratio in RATIO1:
    ax1.plot('Time', ratio, data=NM_CC, color=color[RATIO1.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
    ax1.set_ylabel('Ratio', fontsize=15)
    ax1.legend(loc='best')
plt.show()



fig, axes = plt.subplots(6,1,figsize=(15,48))
color = ['b', 'g', 'r', 'c', 'm']


for RATIO in RATIOS:
    for category in categories:
        axes[RATIOS.index(RATIO)].plot('Time', category, data=NM_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
        axes[RATIOS.index(RATIO)].set_xlabel('Date', fontsize=15)
        axes[RATIOS.index(RATIO)].set_ylabel('The number of PPE', fontsize=15)
        axes[RATIOS.index(RATIO)].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
        axes[RATIOS.index(RATIO)].legend(loc='best')
        axes[RATIOS.index(RATIO)].set_title('The Ratio of PPE Dstribution to Confirmed Cases in Northeastern Massachusetts', fontsize=20)
    ax1 = axes[RATIOS.index(RATIO)].twinx()
    for ratio in RATIO:
        ax1.plot('Time', ratio, data=NM_CC, color=color[RATIO.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
        ax1.set_ylabel('Ratio', fontsize=15)
        ax1.legend(loc='best')
plt.show()

for category in categories:
    NM_CC['{} delta'.format(category)] = NM_CC[category].diff()
    NM_CC['Confirmed Cases delta'] = NM_CC['Confirmed Cases'].diff()

fig, axes = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes.flatten(), categories):
    sns.regplot('Confirmed Cases', category, data=NM_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig.delaxes(axes[1,2])
fig.suptitle('Regression Plot (Cumulative vs Cumulative)', fontsize=25)

fig1, axes1 = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes1.flatten(), categories):
    sns.regplot('Confirmed Cases delta', category + ' delta', data=NM_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig1.delaxes(axes1[1,2])
fig1.suptitle('Regression Plot (Delta vs Delta)', fontsize=25)

Text(0.5, 0.98, 'Regression Plot (Delta vs Delta)')

import statsmodels.api as sm
Y = [pd.DataFrame] * 5
X = [pd.DataFrame] * 5
for category in categories:
    Y[categories.index(category)] = NM_CC[category]
    X[categories.index(category)] = NM_CC['Confirmed Cases']
    X[categories.index(category)] = sm.add_constant(X[categories.index(category)])
    model = sm.OLS(Y[categories.index(category)], X[categories.index(category)])
    results = model.fit()
    print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:             N95s/KN95s   R-squared:                       0.542
Model:                            OLS   Adj. R-squared:                  0.517
Method:                 Least Squares   F-statistic:                     21.32
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           0.000214
Time:                        20:58:07   Log-Likelihood:                -281.72
No. Observations:                  20   AIC:                             567.4
Df Residuals:                      18   BIC:                             569.4
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.692e+06   4.89e+05     -3.459      0.003   -2.72e+06   -6.64e+05
Confirmed Cases    87.9436     19.047      4.617      0.000      47.928     127.959
==============================================================================
Omnibus:                        8.473   Durbin-Watson:                   0.190
Prob(Omnibus):                  0.014   Jarque-Bera (JB):                1.909
Skew:                           0.032   Prob(JB):                        0.385
Kurtosis:                       1.488   Cond. No.                     1.68e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.68e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Masks   R-squared:                       0.579
Model:                            OLS   Adj. R-squared:                  0.556
Method:                 Least Squares   F-statistic:                     24.80
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           9.69e-05
Time:                        20:58:07   Log-Likelihood:                -284.27
No. Observations:                  20   AIC:                             572.5
Df Residuals:                      18   BIC:                             574.5
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.956e+06   5.56e+05     -3.520      0.002   -3.12e+06   -7.89e+05
Confirmed Cases   107.7427     21.635      4.980      0.000      62.290     153.196
==============================================================================
Omnibus:                        9.258   Durbin-Watson:                   0.193
Prob(Omnibus):                  0.010   Jarque-Bera (JB):                1.974
Skew:                           0.013   Prob(JB):                        0.373
Kurtosis:                       1.461   Cond. No.                     1.68e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.68e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Gowns   R-squared:                       0.951
Model:                            OLS   Adj. R-squared:                  0.949
Method:                 Least Squares   F-statistic:                     352.4
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           2.87e-13
Time:                        20:58:07   Log-Likelihood:                -183.01
No. Observations:                  20   AIC:                             370.0
Df Residuals:                      18   BIC:                             372.0
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.758e+04   3515.045     -5.000      0.000    -2.5e+04   -1.02e+04
Confirmed Cases     2.5693      0.137     18.771      0.000       2.282       2.857
==============================================================================
Omnibus:                        1.546   Durbin-Watson:                   0.404
Prob(Omnibus):                  0.462   Jarque-Bera (JB):                1.014
Skew:                          -0.225   Prob(JB):                        0.602
Kurtosis:                       1.993   Cond. No.                     1.68e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.68e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Gloves   R-squared:                       0.955
Model:                            OLS   Adj. R-squared:                  0.952
Method:                 Least Squares   F-statistic:                     379.5
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.52e-13
Time:                        20:58:07   Log-Likelihood:                -231.77
No. Observations:                  20   AIC:                             467.5
Df Residuals:                      18   BIC:                             469.5
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -9.519e+04   4.02e+04     -2.365      0.029    -1.8e+05   -1.06e+04
Confirmed Cases    30.5327      1.567     19.481      0.000      27.240      33.825
==============================================================================
Omnibus:                        1.301   Durbin-Watson:                   0.329
Prob(Omnibus):                  0.522   Jarque-Bera (JB):                0.869
Skew:                          -0.109   Prob(JB):                        0.648
Kurtosis:                       2.002   Cond. No.                     1.68e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.68e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:            Ventilators   R-squared:                        -inf
Model:                            OLS   Adj. R-squared:                   -inf
Method:                 Least Squares   F-statistic:                    -18.00
Date:                Sun, 13 Sep 2020   Prob (F-statistic):               1.00
Time:                        20:58:07   Log-Likelihood:                 587.29
No. Observations:                  20   AIC:                            -1171.
Df Residuals:                      18   BIC:                            -1169.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const             117.0000   6.59e-14   1.77e+15      0.000     117.000     117.000
Confirmed Cases  1.735e-18   2.57e-18      0.676      0.508   -3.66e-18    7.13e-18
==============================================================================
Omnibus:                        0.186   Durbin-Watson:                   0.011
Prob(Omnibus):                  0.911   Jarque-Bera (JB):                0.017
Skew:                           0.011   Prob(JB):                        0.992
Kurtosis:                       2.859   Cond. No.                     1.68e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.68e+05. This might indicate that there are
strong multicollinearity or other numerical problems.

Metro West¶

Metro West is the region with the most deaths number.¶

MW = df.loc[df['Region']=='Metro West']
MW_CC = MW.dropna()
categories = ['N95s/KN95s', 'Masks', 'Gowns', 'Gloves', 'Ventilators']
for category in categories:
    MW_CC['{} %'.format(category)] = MW_CC[category]/MW_CC[category].iloc[-1] * 100
    
fig, axes = plt.subplots(2, 3, figsize=(25,15))
for ax, category in zip(axes.flatten(), categories):
    sns.distplot(MW_CC[category].diff(), ax=ax)
    ax.set_xlabel(category, fontsize=15)
fig.delaxes(axes[1,2])
MW_CC

# Confirmed cases vs PPE distribution
fig, axes = plt.subplots(2,1,figsize=(15,14))

percentages = ['N95s/KN95s %', 'Masks %', 'Gowns %', 'Gloves %', 'Ventilators %']

for category in categories:
    axes[0].plot('Time', category, data=MW_CC, marker='.', markersize=10)

axes[0].set_xlabel('Date', fontsize=15)
axes[0].set_ylabel('The Number of PPE Distribution', fontsize=15)
axes[0].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[0].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[0].legend()
axes[0].set_title('PPE Distribution vs Confirmed Cases in MW', fontsize=20)

ax1 = axes[0].twinx()
ax1.plot('Time', 'Confirmed Cases', data=MW_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax1.set_ylabel('Confirmed Cases', fontsize=15)
ax1.tick_params(direction='out', length=6, width=2, labelsize=10)
ax1.legend(loc='upper right')


for percentage in percentages:
    axes[1].plot('Time', percentage, data=MW_CC, marker='.', markersize=10)

axes[1].set_xlabel('Date', fontsize=15)
axes[1].set_ylabel('PPE Distribution %', fontsize=15)
axes[1].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[1].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[1].legend()
axes[1].set_title('PPE Distribution Percentage vs Confirmed Cases in MW', fontsize=20)

ax2 = axes[1].twinx()
ax2.plot('Time', 'Confirmed Cases', data=MW_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax2.set_ylabel('Confirmed Cases', fontsize=15)
ax2.tick_params(direction='out', length=6, width=2, labelsize=10)
ax2.legend(loc='upper right')

plt.show()

pd.set_option("display.max_rows", None, "display.max_columns", None)


# Calculate the ratio of cumulative PPE data to cumulative confirmed cases data (Ratio 1)
for category in categories:
    MW_CC['{} Ratio 1'.format(category)] = MW_CC[category]/MW_CC['Confirmed Cases']
    
    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 2)
# (Interval=2 weeks, one week before + the week itself)
MW_CC['Confirmed Cases(ma1)'] = MW_CC['Confirmed Cases'].rolling(window=2,center=False).mean()
MW_CC.iloc[0,-1] = (9219+12601) / 2
for category in categories:
    MW_CC['{} Ratio 2'.format(category)] = MW_CC[category]/MW_CC['Confirmed Cases(ma1)']





# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 3)
# (Interval=2 weeks, one week after + the week itself)
MW_CC['Confirmed Cases(ma2)'] = MW_CC['Confirmed Cases'].rolling(window=2,center=False).mean().shift(-1)
for category in categories:
    MW_CC['{} Ratio 3'.format(category)] = MW_CC[category]/MW_CC['Confirmed Cases(ma2)']

    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 4)
# (Interval=3 weeks, one week before + one week after + the week itself)
MW_CC['Confirmed Cases(ma3)'] = MW_CC['Confirmed Cases'].rolling(window=3,center=False).mean().shift(-1)
MW_CC.iloc[0,-1] = (9219+12601+14825)/3
for category in categories:
    MW_CC['{} Ratio 4'.format(category)] = MW_CC[category]/MW_CC['Confirmed Cases(ma3)']

    
    
# Calculate the ratio of delta PPE data to delta confirmed cases data (Ratio 5)
for category in categories:
    MW_CC['{} Ratio 5'.format(category)] = MW_CC[category].diff()/MW_CC['Confirmed Cases'].diff()
    
    
    
# Calculate the ratio of delta PPE data to moving average delta confirmed cases data (Ratio 6)
for category in categories:
    MW_CC['{} Ratio 6'.format(category)] = MW_CC[category].diff()/MW_CC['Confirmed Cases'].diff().rolling(window=2,center=False).mean()
MW_CC

# Plot the Ratio of PPE to Confirmed Cases

RATIO1 = []
RATIO2 = []
RATIO3 = []
RATIO4 = []
RATIO5 = []
RATIO6 = []
RATIOS = [RATIO1, RATIO2, RATIO3, RATIO4, RATIO5, RATIO6]
for RATIO in RATIOS:
    for category in categories:
        RATIO.append('{} Ratio {}'.format(category, RATIOS.index(RATIO)+1))
        
        
fig, ax = plt.subplots(1,1,figsize=(14, 6))
color = ['b', 'g', 'r', 'c', 'm']

for category in categories:
    ax.plot('Time', category, data=MW_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
    ax.set_xlabel('Date', fontsize=15)
    ax.set_ylabel('The number of PPE', fontsize=15)
    ax.set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
    ax.legend(loc='best')
    ax.set_title('The Ratio of PPE Dstribution to Confirmed Cases in Southeastern Massachusetts', fontsize=20)
ax1 = ax.twinx()
for ratio in RATIO1:
    ax1.plot('Time', ratio, data=MW_CC, color=color[RATIO1.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
    ax1.set_ylabel('Ratio', fontsize=15)
    ax1.legend(loc='best')
plt.show()


fig, axes = plt.subplots(6,1,figsize=(15,48))
color = ['b', 'g', 'r', 'c', 'm']


for RATIO in RATIOS:
    for category in categories:
        axes[RATIOS.index(RATIO)].plot('Time', category, data=MW_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
        axes[RATIOS.index(RATIO)].set_xlabel('Date', fontsize=15)
        axes[RATIOS.index(RATIO)].set_ylabel('The number of PPE', fontsize=15)
        axes[RATIOS.index(RATIO)].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
        axes[RATIOS.index(RATIO)].legend(loc='best')
        axes[RATIOS.index(RATIO)].set_title('The Ratio of PPE Dstribution to Confirmed Cases in Metro West', fontsize=20)
    ax1 = axes[RATIOS.index(RATIO)].twinx()
    for ratio in RATIO:
        ax1.plot('Time', ratio, data=MW_CC, color=color[RATIO.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
        ax1.set_ylabel('Ratio', fontsize=15)
        ax1.legend(loc='best')
plt.show()

for category in categories:
    MW_CC['{} delta'.format(category)] = MW_CC[category].diff()
    MW_CC['Confirmed Cases delta'] = MW_CC['Confirmed Cases'].diff()

fig, axes = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes.flatten(), categories):
    sns.regplot('Confirmed Cases', category, data=MW_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig.delaxes(axes[1,2])
fig.suptitle('Regression Plot (Cumulative vs Cumulative)', fontsize=25)

fig1, axes1 = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes1.flatten(), categories):
    sns.regplot('Confirmed Cases delta', category + ' delta', data=MW_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig1.delaxes(axes1[1,2])
fig1.suptitle('Regression Plot (Delta vs Delta)', fontsize=25)

Text(0.5, 0.98, 'Regression Plot (Delta vs Delta)')

import statsmodels.api as sm
Y = [pd.DataFrame] * 5
X = [pd.DataFrame] * 5
for category in categories:
    Y[categories.index(category)] = MW_CC[category]
    X[categories.index(category)] = MW_CC['Confirmed Cases']
    X[categories.index(category)] = sm.add_constant(X[categories.index(category)])
    model = sm.OLS(Y[categories.index(category)], X[categories.index(category)])
    results = model.fit()
    print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:             N95s/KN95s   R-squared:                       0.609
Model:                            OLS   Adj. R-squared:                  0.587
Method:                 Least Squares   F-statistic:                     28.00
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.97e-05
Time:                        20:58:14   Log-Likelihood:                -283.76
No. Observations:                  20   AIC:                             571.5
Df Residuals:                      18   BIC:                             573.5
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const            -2.54e+06   6.48e+05     -3.920      0.001    -3.9e+06   -1.18e+06
Confirmed Cases   173.0176     32.699      5.291      0.000     104.318     241.717
==============================================================================
Omnibus:                        7.908   Durbin-Watson:                   0.209
Prob(Omnibus):                  0.019   Jarque-Bera (JB):                1.930
Skew:                           0.163   Prob(JB):                        0.381
Kurtosis:                       1.513   Cond. No.                     1.55e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.55e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Masks   R-squared:                       0.622
Model:                            OLS   Adj. R-squared:                  0.601
Method:                 Least Squares   F-statistic:                     29.58
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           3.63e-05
Time:                        20:58:14   Log-Likelihood:                -285.20
No. Observations:                  20   AIC:                             574.4
Df Residuals:                      18   BIC:                             576.4
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -2.638e+06   6.96e+05     -3.789      0.001    -4.1e+06   -1.17e+06
Confirmed Cases   191.0928     35.138      5.438      0.000     117.271     264.914
==============================================================================
Omnibus:                        8.311   Durbin-Watson:                   0.194
Prob(Omnibus):                  0.016   Jarque-Bera (JB):                1.945
Skew:                           0.136   Prob(JB):                        0.378
Kurtosis:                       1.497   Cond. No.                     1.55e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.55e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Gowns   R-squared:                       0.940
Model:                            OLS   Adj. R-squared:                  0.937
Method:                 Least Squares   F-statistic:                     284.0
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.81e-12
Time:                        20:58:14   Log-Likelihood:                -212.87
No. Observations:                  20   AIC:                             429.7
Df Residuals:                      18   BIC:                             431.7
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.698e+05   1.87e+04     -9.077      0.000   -2.09e+05   -1.31e+05
Confirmed Cases    15.9169      0.944     16.854      0.000      13.933      17.901
==============================================================================
Omnibus:                        4.513   Durbin-Watson:                   0.928
Prob(Omnibus):                  0.105   Jarque-Bera (JB):                2.497
Skew:                          -0.801   Prob(JB):                        0.287
Kurtosis:                       3.657   Cond. No.                     1.55e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.55e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Gloves   R-squared:                       0.973
Model:                            OLS   Adj. R-squared:                  0.971
Method:                 Least Squares   F-statistic:                     639.0
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.63e-15
Time:                        20:58:14   Log-Likelihood:                -244.43
No. Observations:                  20   AIC:                             492.9
Df Residuals:                      18   BIC:                             494.9
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.124e+06   9.07e+04    -12.392      0.000   -1.31e+06   -9.33e+05
Confirmed Cases   115.6837      4.576     25.279      0.000     106.069     125.298
==============================================================================
Omnibus:                        0.827   Durbin-Watson:                   0.455
Prob(Omnibus):                  0.661   Jarque-Bera (JB):                0.749
Skew:                          -0.192   Prob(JB):                        0.688
Kurtosis:                       2.134   Cond. No.                     1.55e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.55e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:            Ventilators   R-squared:                        -inf
Model:                            OLS   Adj. R-squared:                   -inf
Method:                 Least Squares   F-statistic:                    -18.00
Date:                Sun, 13 Sep 2020   Prob (F-statistic):               1.00
Time:                        20:58:14   Log-Likelihood:                 573.48
No. Observations:                  20   AIC:                            -1143.
Df Residuals:                      18   BIC:                            -1141.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const             136.0000   1.57e-13   8.65e+14      0.000     136.000     136.000
Confirmed Cases -1.735e-18   7.94e-18     -0.219      0.829   -1.84e-17    1.49e-17
==============================================================================
Omnibus:                      466.626   Durbin-Watson:                   0.000
Prob(Omnibus):                  0.000   Jarque-Bera (JB):                7.500
Skew:                           0.000   Prob(JB):                       0.0235
Kurtosis:                       0.000   Cond. No.                     1.55e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.55e+05. This might indicate that there are
strong multicollinearity or other numerical problems.

Metro Boston¶

MB = df.loc[df['Region']=='Metro Boston']
MB_CC = MB.dropna()
categories = ['N95s/KN95s', 'Masks', 'Gowns', 'Gloves', 'Ventilators']
for category in categories:
    MB_CC['{} %'.format(category)] = MB_CC[category]/MB_CC[category].iloc[-1] * 100
    
fig, axes = plt.subplots(2, 3, figsize=(25,15))
for ax, category in zip(axes.flatten(), categories):
    sns.distplot(MB_CC[category].diff(), ax=ax)
    ax.set_xlabel(category, fontsize=15)
fig.delaxes(axes[1,2])
MB_CC

# Confirmed cases vs PPE distribution
fig, axes = plt.subplots(2,1,figsize=(15,14))

percentages = ['N95s/KN95s %', 'Masks %', 'Gowns %', 'Gloves %', 'Ventilators %']

for category in categories:
    axes[0].plot('Time', category, data=MB_CC, marker='.', markersize=10)

axes[0].set_xlabel('Date', fontsize=15)
axes[0].set_ylabel('The Number of PPE Distribution', fontsize=15)
axes[0].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[0].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[0].legend()
axes[0].set_title('PPE Distribution vs Confirmed Cases in MB', fontsize=20)

ax1 = axes[0].twinx()
ax1.plot('Time', 'Confirmed Cases', data=MB_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax1.set_ylabel('Confirmed Cases', fontsize=15)
ax1.tick_params(direction='out', length=6, width=2, labelsize=10)
ax1.legend(loc='upper right')


for percentage in percentages:
    axes[1].plot('Time', percentage, data=MB_CC, marker='.', markersize=10)

axes[1].set_xlabel('Date', fontsize=15)
axes[1].set_ylabel('PPE Distribution %', fontsize=15)
axes[1].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[1].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[1].legend()
axes[1].set_title('PPE Distribution Percentage vs Confirmed Cases in MB', fontsize=20)

ax2 = axes[1].twinx()
ax2.plot('Time', 'Confirmed Cases', data=MB_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax2.set_ylabel('Confirmed Cases', fontsize=15)
ax2.tick_params(direction='out', length=6, width=2, labelsize=10)
ax2.legend(loc='upper right')

plt.show()

pd.set_option("display.max_rows", None, "display.max_columns", None)


# Calculate the ratio of cumulative PPE data to cumulative confirmed cases data (Ratio 1)
for category in categories:
    MB_CC['{} Ratio 1'.format(category)] = MB_CC[category]/MB_CC['Confirmed Cases']
    
    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 2)
# (Interval=2 weeks, one week before + the week itself)
MB_CC['Confirmed Cases(ma1)'] = MB_CC['Confirmed Cases'].rolling(window=2,center=False).mean()
MB_CC.iloc[0,-1] = (9060+12539) / 2
for category in categories:
    MB_CC['{} Ratio 2'.format(category)] = MB_CC[category]/MB_CC['Confirmed Cases(ma1)']





# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 3)
# (Interval=2 weeks, one week after + the week itself)
MB_CC['Confirmed Cases(ma2)'] = MB_CC['Confirmed Cases'].rolling(window=2,center=False).mean().shift(-1)
for category in categories:
    MB_CC['{} Ratio 3'.format(category)] = MB_CC[category]/MB_CC['Confirmed Cases(ma2)']

    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 4)
# (Interval=3 weeks, one week before + one week after + the week itself)
MB_CC['Confirmed Cases(ma3)'] = MB_CC['Confirmed Cases'].rolling(window=3,center=False).mean().shift(-1)
MB_CC.iloc[0,-1] = (9060+12539+14476)/3
for category in categories:
    MB_CC['{} Ratio 4'.format(category)] = MB_CC[category]/MB_CC['Confirmed Cases(ma3)']

    
    
# Calculate the ratio of delta PPE data to delta confirmed cases data (Ratio 5)
for category in categories:
    MB_CC['{} Ratio 5'.format(category)] = MB_CC[category].diff()/MB_CC['Confirmed Cases'].diff()
    
    
    
# Calculate the ratio of delta PPE data to moving average delta confirmed cases data (Ratio 6)
for category in categories:
    MB_CC['{} Ratio 6'.format(category)] = MB_CC[category].diff()/MB_CC['Confirmed Cases'].diff().rolling(window=2,center=False).mean()
MB_CC

# Plot the Ratio of PPE to Confirmed Cases

RATIO1 = []
RATIO2 = []
RATIO3 = []
RATIO4 = []
RATIO5 = []
RATIO6 = []
RATIOS = [RATIO1, RATIO2, RATIO3, RATIO4, RATIO5, RATIO6]
for RATIO in RATIOS:
    for category in categories:
        RATIO.append('{} Ratio {}'.format(category, RATIOS.index(RATIO)+1))

fig, axes = plt.subplots(6,1,figsize=(15,48))
color = ['b', 'g', 'r', 'c', 'm']


for RATIO in RATIOS:
    for category in categories:
        axes[RATIOS.index(RATIO)].plot('Time', category, data=MB_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
        axes[RATIOS.index(RATIO)].set_xlabel('Date', fontsize=15)
        axes[RATIOS.index(RATIO)].set_ylabel('The number of PPE', fontsize=15)
        axes[RATIOS.index(RATIO)].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
        axes[RATIOS.index(RATIO)].legend(loc='best')
        axes[RATIOS.index(RATIO)].set_title('The Ratio of PPE Dstribution to Confirmed Cases in Metro Boston', fontsize=20)
    ax1 = axes[RATIOS.index(RATIO)].twinx()
    for ratio in RATIO:
        ax1.plot('Time', ratio, data=MB_CC, color=color[RATIO.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
        ax1.set_ylabel('Ratio', fontsize=15)
        ax1.legend(loc='best')
plt.show()

for category in categories:
    MB_CC['{} delta'.format(category)] = MB_CC[category].diff()
    MB_CC['Confirmed Cases delta'] = MB_CC['Confirmed Cases'].diff()

fig, axes = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes.flatten(), categories):
    sns.regplot('Confirmed Cases', category, data=MB_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig.delaxes(axes[1,2])
fig.suptitle('Regression Plot (Cumulative vs Cumulative)', fontsize=25)

fig1, axes1 = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes1.flatten(), categories):
    sns.regplot('Confirmed Cases delta', category + ' delta', data=MB_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig1.delaxes(axes1[1,2])
fig1.suptitle('Regression Plot (Delta vs Delta)', fontsize=25)

Text(0.5, 0.98, 'Regression Plot (Delta vs Delta)')

import statsmodels.api as sm
Y = [pd.DataFrame] * 5
X = [pd.DataFrame] * 5
for category in categories:
    Y[categories.index(category)] = MB_CC[category]
    X[categories.index(category)] = MB_CC['Confirmed Cases']
    X[categories.index(category)] = sm.add_constant(X[categories.index(category)])
    model = sm.OLS(Y[categories.index(category)], X[categories.index(category)])
    results = model.fit()
    print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:             N95s/KN95s   R-squared:                       0.706
Model:                            OLS   Adj. R-squared:                  0.689
Method:                 Least Squares   F-statistic:                     43.13
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           3.60e-06
Time:                        20:58:22   Log-Likelihood:                -248.62
No. Observations:                  20   AIC:                             501.2
Df Residuals:                      18   BIC:                             503.2
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -4.578e+05   1.07e+05     -4.276      0.000   -6.83e+05   -2.33e+05
Confirmed Cases    36.8346      5.609      6.567      0.000      25.051      48.618
==============================================================================
Omnibus:                        7.017   Durbin-Watson:                   0.439
Prob(Omnibus):                  0.030   Jarque-Bera (JB):                1.797
Skew:                           0.102   Prob(JB):                        0.407
Kurtosis:                       1.546   Cond. No.                     1.43e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.43e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Masks   R-squared:                       0.773
Model:                            OLS   Adj. R-squared:                  0.761
Method:                 Least Squares   F-statistic:                     61.34
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           3.31e-07
Time:                        20:58:22   Log-Likelihood:                -248.96
No. Observations:                  20   AIC:                             501.9
Df Residuals:                      18   BIC:                             503.9
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -5.181e+05   1.09e+05     -4.758      0.000   -7.47e+05   -2.89e+05
Confirmed Cases    44.6811      5.705      7.832      0.000      32.695      56.667
==============================================================================
Omnibus:                        3.450   Durbin-Watson:                   0.479
Prob(Omnibus):                  0.178   Jarque-Bera (JB):                1.651
Skew:                           0.374   Prob(JB):                        0.438
Kurtosis:                       1.808   Cond. No.                     1.43e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.43e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Gowns   R-squared:                       0.889
Model:                            OLS   Adj. R-squared:                  0.882
Method:                 Least Squares   F-statistic:                     143.6
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           5.16e-10
Time:                        20:58:22   Log-Likelihood:                -189.14
No. Observations:                  20   AIC:                             382.3
Df Residuals:                      18   BIC:                             384.3
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const            -1.81e+04   5468.315     -3.310      0.004   -2.96e+04   -6612.584
Confirmed Cases     3.4333      0.287     11.984      0.000       2.831       4.035
==============================================================================
Omnibus:                        2.360   Durbin-Watson:                   0.213
Prob(Omnibus):                  0.307   Jarque-Bera (JB):                1.563
Skew:                          -0.461   Prob(JB):                        0.458
Kurtosis:                       1.987   Cond. No.                     1.43e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.43e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Gloves   R-squared:                       0.974
Model:                            OLS   Adj. R-squared:                  0.973
Method:                 Least Squares   F-statistic:                     680.2
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           9.45e-16
Time:                        20:58:22   Log-Likelihood:                -218.93
No. Observations:                  20   AIC:                             441.9
Df Residuals:                      18   BIC:                             443.9
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const            6.376e+04   2.43e+04      2.629      0.017    1.28e+04    1.15e+05
Confirmed Cases    33.1437      1.271     26.080      0.000      30.474      35.814
==============================================================================
Omnibus:                        0.549   Durbin-Watson:                   1.124
Prob(Omnibus):                  0.760   Jarque-Bera (JB):                0.126
Skew:                          -0.195   Prob(JB):                        0.939
Kurtosis:                       2.993   Cond. No.                     1.43e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.43e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:            Ventilators   R-squared:                        -inf
Model:                            OLS   Adj. R-squared:                   -inf
Method:                 Least Squares   F-statistic:                    -18.00
Date:                Sun, 13 Sep 2020   Prob (F-statistic):               1.00
Time:                        20:58:22   Log-Likelihood:                 587.34
No. Observations:                  20   AIC:                            -1171.
Df Residuals:                      18   BIC:                            -1169.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const             204.0000   7.53e-14   2.71e+15      0.000     204.000     204.000
Confirmed Cases  6.939e-18   3.95e-18      1.759      0.096   -1.35e-18    1.52e-17
==============================================================================
Omnibus:                        2.366   Durbin-Watson:                   0.067
Prob(Omnibus):                  0.306   Jarque-Bera (JB):                1.133
Skew:                           0.566   Prob(JB):                        0.567
Kurtosis:                       3.279   Cond. No.                     1.43e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.43e+05. This might indicate that there are
strong multicollinearity or other numerical problems.

Central Massachusetts¶

CM = df.loc[df['Region']=='Central Massachusetts']
CM_CC = CM.dropna()
categories = ['N95s/KN95s', 'Masks', 'Gowns', 'Gloves', 'Ventilators']
for category in categories:
    CM_CC['{} %'.format(category)] = CM_CC[category]/CM_CC[category].iloc[-1] * 100
    
# fig, axes = plt.subplots(2, 3, figsize=(25,15))
# for ax, category in zip(axes.flatten(), categories):
#     sns.distplot(CM_CC[category].diff(), ax=ax)
#     ax.set_xlabel(category, fontsize=15)
# fig.delaxes(axes[1,2])
CM_CC

# Confirmed cases vs PPE distribution
fig, axes = plt.subplots(2,1,figsize=(15,14))

percentages = ['N95s/KN95s %', 'Masks %', 'Gowns %', 'Gloves %', 'Ventilators %']

for category in categories:
    axes[0].plot('Time', category, data=CM_CC, marker='.', markersize=10)

axes[0].set_xlabel('Date', fontsize=15)
axes[0].set_ylabel('The Number of PPE Distribution', fontsize=15)
axes[0].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[0].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[0].legend()
axes[0].set_title('PPE Distribution vs Confirmed Cases in CM', fontsize=20)

ax1 = axes[0].twinx()
ax1.plot('Time', 'Confirmed Cases', data=CM_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax1.set_ylabel('Confirmed Cases', fontsize=15)
ax1.tick_params(direction='out', length=6, width=2, labelsize=10)
ax1.legend(loc='upper right')


for percentage in percentages:
    axes[1].plot('Time', percentage, data=CM_CC, marker='.', markersize=10)

axes[1].set_xlabel('Date', fontsize=15)
axes[1].set_ylabel('PPE Distribution %', fontsize=15)
axes[1].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[1].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[1].legend()
axes[1].set_title('PPE Distribution Percentage vs Confirmed Cases in CM', fontsize=20)

ax2 = axes[1].twinx()
ax2.plot('Time', 'Confirmed Cases', data=CM_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax2.set_ylabel('Confirmed Cases', fontsize=15)
ax2.tick_params(direction='out', length=6, width=2, labelsize=10)
ax2.legend(loc='upper right')

plt.show()

pd.set_option("display.max_rows", None, "display.max_columns", None)


# Calculate the ratio of cumulative PPE data to cumulative confirmed cases data (Ratio 1)
for category in categories:
    CM_CC['{} Ratio 1'.format(category)] = CM_CC[category]/CM_CC['Confirmed Cases']
    
    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 2)
# (Interval=2 weeks, one week before + the week itself)
CM_CC['Confirmed Cases(ma1)'] = CM_CC['Confirmed Cases'].rolling(window=2,center=False).mean()
CM_CC.iloc[0,-1] = (3474+5363) / 2
for category in categories:
    CM_CC['{} Ratio 2'.format(category)] = CM_CC[category]/CM_CC['Confirmed Cases(ma1)']





# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 3)
# (Interval=2 weeks, one week after + the week itself)
CM_CC['Confirmed Cases(ma2)'] = CM_CC['Confirmed Cases'].rolling(window=2,center=False).mean().shift(-1)
for category in categories:
    CM_CC['{} Ratio 3'.format(category)] = CM_CC[category]/CM_CC['Confirmed Cases(ma2)']

    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 4)
# (Interval=3 weeks, one week before + one week after + the week itself)
CM_CC['Confirmed Cases(ma3)'] = CM_CC['Confirmed Cases'].rolling(window=3,center=False).mean().shift(-1)
CM_CC.iloc[0,-1] = (3474+5363+7056)/3
for category in categories:
    CM_CC['{} Ratio 4'.format(category)] = CM_CC[category]/CM_CC['Confirmed Cases(ma3)']

    
    
# Calculate the ratio of delta PPE data to delta confirmed cases data (Ratio 5)
for category in categories:
    CM_CC['{} Ratio 5'.format(category)] = CM_CC[category].diff()/CM_CC['Confirmed Cases'].diff()
    
    
    
# Calculate the ratio of delta PPE data to moving average delta confirmed cases data (Ratio 6)
for category in categories:
    CM_CC['{} Ratio 6'.format(category)] = CM_CC[category].diff()/CM_CC['Confirmed Cases'].diff().rolling(window=2,center=False).mean()
CM_CC

# Plot the Ratio of PPE to Confirmed Cases

RATIO1 = []
RATIO2 = []
RATIO3 = []
RATIO4 = []
RATIO5 = []
RATIO6 = []
RATIOS = [RATIO1, RATIO2, RATIO3, RATIO4, RATIO5, RATIO6]
for RATIO in RATIOS:
    for category in categories:
        RATIO.append('{} Ratio {}'.format(category, RATIOS.index(RATIO)+1))

fig, axes = plt.subplots(6,1,figsize=(15,48))
color = ['b', 'g', 'r', 'c', 'm']


for RATIO in RATIOS:
    for category in categories:
        axes[RATIOS.index(RATIO)].plot('Time', category, data=CM_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
        axes[RATIOS.index(RATIO)].set_xlabel('Date', fontsize=15)
        axes[RATIOS.index(RATIO)].set_ylabel('The number of PPE', fontsize=15)
        axes[RATIOS.index(RATIO)].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
        axes[RATIOS.index(RATIO)].legend(loc='best')
        axes[RATIOS.index(RATIO)].set_title('The Ratio of PPE Dstribution to Confirmed Cases in Central Massachusetts', fontsize=20)
    ax1 = axes[RATIOS.index(RATIO)].twinx()
    for ratio in RATIO:
        ax1.plot('Time', ratio, data=CM_CC, color=color[RATIO.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
        ax1.set_ylabel('Ratio', fontsize=15)
        ax1.legend(loc='best')
plt.show()

for category in categories:
    CM_CC['{} delta'.format(category)] = CM_CC[category].diff()
    CM_CC['Confirmed Cases delta'] = CM_CC['Confirmed Cases'].diff()

fig, axes = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes.flatten(), categories):
    sns.regplot('Confirmed Cases', category, data=CM_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig.delaxes(axes[1,2])
fig.suptitle('Regression Plot (Cumulative vs Cumulative)', fontsize=25)

fig1, axes1 = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes1.flatten(), categories):
    sns.regplot('Confirmed Cases delta', category + ' delta', data=CM_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig1.delaxes(axes1[1,2])
fig1.suptitle('Regression Plot (Delta vs Delta)', fontsize=25)

Text(0.5, 0.98, 'Regression Plot (Delta vs Delta)')

import statsmodels.api as sm
Y = [pd.DataFrame] * 5
X = [pd.DataFrame] * 5
for category in categories:
    Y[categories.index(category)] = CM_CC[category]
    X[categories.index(category)] = CM_CC['Confirmed Cases']
    X[categories.index(category)] = sm.add_constant(X[categories.index(category)])
    model = sm.OLS(Y[categories.index(category)], X[categories.index(category)])
    results = model.fit()
    print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:             N95s/KN95s   R-squared:                       0.433
Model:                            OLS   Adj. R-squared:                  0.402
Method:                 Least Squares   F-statistic:                     13.76
Date:                Sun, 13 Sep 2020   Prob (F-statistic):            0.00160
Time:                        20:58:28   Log-Likelihood:                -278.18
No. Observations:                  20   AIC:                             560.4
Df Residuals:                      18   BIC:                             562.4
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -7.393e+05   3.32e+05     -2.228      0.039   -1.44e+06   -4.21e+04
Confirmed Cases   104.7657     28.239      3.710      0.002      45.437     164.094
==============================================================================
Omnibus:                        6.118   Durbin-Watson:                   0.165
Prob(Omnibus):                  0.047   Jarque-Bera (JB):                2.252
Skew:                           0.460   Prob(JB):                        0.324
Kurtosis:                       1.638   Cond. No.                     6.22e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.22e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Masks   R-squared:                       0.502
Model:                            OLS   Adj. R-squared:                  0.474
Method:                 Least Squares   F-statistic:                     18.15
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           0.000471
Time:                        20:58:28   Log-Likelihood:                -279.13
No. Observations:                  20   AIC:                             562.3
Df Residuals:                      18   BIC:                             564.3
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -7.619e+05   3.48e+05     -2.190      0.042   -1.49e+06   -3.11e+04
Confirmed Cases   126.1192     29.606      4.260      0.000      63.918     188.320
==============================================================================
Omnibus:                        6.526   Durbin-Watson:                   0.170
Prob(Omnibus):                  0.038   Jarque-Bera (JB):                2.247
Skew:                           0.437   Prob(JB):                        0.325
Kurtosis:                       1.610   Cond. No.                     6.22e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.22e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Gowns   R-squared:                       0.947
Model:                            OLS   Adj. R-squared:                  0.944
Method:                 Least Squares   F-statistic:                     320.4
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           6.48e-13
Time:                        20:58:28   Log-Likelihood:                -193.92
No. Observations:                  20   AIC:                             391.8
Df Residuals:                      18   BIC:                             393.8
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -4810.8419   4911.509     -0.980      0.340   -1.51e+04    5507.855
Confirmed Cases     7.4820      0.418     17.900      0.000       6.604       8.360
==============================================================================
Omnibus:                        0.080   Durbin-Watson:                   0.541
Prob(Omnibus):                  0.961   Jarque-Bera (JB):                0.185
Skew:                          -0.123   Prob(JB):                        0.912
Kurtosis:                       2.599   Cond. No.                     6.22e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.22e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Gloves   R-squared:                       0.898
Model:                            OLS   Adj. R-squared:                  0.893
Method:                 Least Squares   F-statistic:                     159.3
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           2.23e-10
Time:                        20:58:28   Log-Likelihood:                -242.58
No. Observations:                  20   AIC:                             489.2
Df Residuals:                      18   BIC:                             491.1
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const            2.764e+05   5.59e+04      4.941      0.000    1.59e+05    3.94e+05
Confirmed Cases    60.0800      4.760     12.622      0.000      50.079      70.081
==============================================================================
Omnibus:                       12.497   Durbin-Watson:                   1.608
Prob(Omnibus):                  0.002   Jarque-Bera (JB):               15.750
Skew:                          -0.926   Prob(JB):                     0.000380
Kurtosis:                       6.934   Cond. No.                     6.22e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.22e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:            Ventilators   R-squared:                       0.768
Model:                            OLS   Adj. R-squared:                  0.755
Method:                 Least Squares   F-statistic:                     59.44
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.13e-07
Time:                        20:58:28   Log-Likelihood:                -23.188
No. Observations:                  20   AIC:                             50.38
Df Residuals:                      18   BIC:                             52.37
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const              74.9076      0.963     77.768      0.000      72.884      76.931
Confirmed Cases     0.0006    8.2e-05      7.709      0.000       0.000       0.001
==============================================================================
Omnibus:                        5.370   Durbin-Watson:                   1.251
Prob(Omnibus):                  0.068   Jarque-Bera (JB):                3.069
Skew:                          -0.846   Prob(JB):                        0.216
Kurtosis:                       3.907   Cond. No.                     6.22e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.22e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

Western Massachusetts¶

WM = df.loc[df['Region']=='Western Massachusetts']
WM_CC = WM.dropna()
categories = ['N95s/KN95s', 'Masks', 'Gowns', 'Gloves', 'Ventilators']
for category in categories:
    WM_CC['{} %'.format(category)] = WM_CC[category]/WM_CC[category].iloc[-1] * 100
    
fig, axes = plt.subplots(2, 3, figsize=(25,15))
for ax, category in zip(axes.flatten(), categories):
    sns.distplot(WM_CC[category].diff(), ax=ax)
    ax.set_xlabel(category, fontsize=15)
fig.delaxes(axes[1,2])
WM_CC

# Confirmed cases vs PPE distribution
fig, axes = plt.subplots(2,1,figsize=(15,14))

percentages = ['N95s/KN95s %', 'Masks %', 'Gowns %', 'Gloves %', 'Ventilators %']

for category in categories:
    axes[0].plot('Time', category, data=WM_CC, marker='.', markersize=10)

axes[0].set_xlabel('Date', fontsize=15)
axes[0].set_ylabel('The Number of PPE Distribution', fontsize=15)
axes[0].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[0].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[0].legend()
axes[0].set_title('PPE Distribution vs Confirmed Cases in WM', fontsize=20)

ax1 = axes[0].twinx()
ax1.plot('Time', 'Confirmed Cases', data=WM_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax1.set_ylabel('Confirmed Cases', fontsize=15)
ax1.tick_params(direction='out', length=6, width=2, labelsize=10)
ax1.legend(loc='upper right')


for percentage in percentages:
    axes[1].plot('Time', percentage, data=WM_CC, marker='.', markersize=10)

axes[1].set_xlabel('Date', fontsize=15)
axes[1].set_ylabel('PPE Distribution %', fontsize=15)
axes[1].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[1].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[1].legend()
axes[1].set_title('PPE Distribution Percentage vs Confirmed Cases in WM', fontsize=20)

ax2 = axes[1].twinx()
ax2.plot('Time', 'Confirmed Cases', data=WM_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax2.set_ylabel('Confirmed Cases', fontsize=15)
ax2.tick_params(direction='out', length=6, width=2, labelsize=10)
ax2.legend(loc='upper right')

plt.show()

pd.set_option("display.max_rows", None, "display.max_columns", None)


# Calculate the ratio of cumulative PPE data to cumulative confirmed cases data (Ratio 1)
for category in categories:
    WM_CC['{} Ratio 1'.format(category)] = WM_CC[category]/WM_CC['Confirmed Cases']
    
    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 2)
# (Interval=2 weeks, one week before + the week itself)
WM_CC['Confirmed Cases(ma1)'] = WM_CC['Confirmed Cases'].rolling(window=2,center=False).mean()
WM_CC.iloc[0,-1] = (3763+5052) / 2
for category in categories:
    WM_CC['{} Ratio 2'.format(category)] = WM_CC[category]/WM_CC['Confirmed Cases(ma1)']





# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 3)
# (Interval=2 weeks, one week after + the week itself)
WM_CC['Confirmed Cases(ma2)'] = WM_CC['Confirmed Cases'].rolling(window=2,center=False).mean().shift(-1)
for category in categories:
    WM_CC['{} Ratio 3'.format(category)] = WM_CC[category]/WM_CC['Confirmed Cases(ma2)']

    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 4)
# (Interval=3 weeks, one week before + one week after + the week itself)
WM_CC['Confirmed Cases(ma3)'] = WM_CC['Confirmed Cases'].rolling(window=3,center=False).mean().shift(-1)
WM_CC.iloc[0,-1] = (3763+5052+5773)/3
for category in categories:
    WM_CC['{} Ratio 4'.format(category)] = WM_CC[category]/WM_CC['Confirmed Cases(ma3)']

    
    
# Calculate the ratio of delta PPE data to delta confirmed cases data (Ratio 5)
for category in categories:
    WM_CC['{} Ratio 5'.format(category)] = WM_CC[category].diff()/WM_CC['Confirmed Cases'].diff()
    
    
    
# Calculate the ratio of delta PPE data to moving average delta confirmed cases data (Ratio 6)
for category in categories:
    WM_CC['{} Ratio 6'.format(category)] = WM_CC[category].diff()/WM_CC['Confirmed Cases'].diff().rolling(window=2,center=False).mean()
WM_CC

# Plot the Ratio of PPE to Confirmed Cases

RATIO1 = []
RATIO2 = []
RATIO3 = []
RATIO4 = []
RATIO5 = []
RATIO6 = []
RATIOS = [RATIO1, RATIO2, RATIO3, RATIO4, RATIO5, RATIO6]
for RATIO in RATIOS:
    for category in categories:
        RATIO.append('{} Ratio {}'.format(category, RATIOS.index(RATIO)+1))

fig, axes = plt.subplots(6,1,figsize=(15,48))
color = ['b', 'g', 'r', 'c', 'm']


for RATIO in RATIOS:
    for category in categories:
        axes[RATIOS.index(RATIO)].plot('Time', category, data=WM_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
        axes[RATIOS.index(RATIO)].set_xlabel('Date', fontsize=15)
        axes[RATIOS.index(RATIO)].set_ylabel('The number of PPE', fontsize=15)
        axes[RATIOS.index(RATIO)].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
        axes[RATIOS.index(RATIO)].legend(loc='best')
        axes[RATIOS.index(RATIO)].set_title('The Ratio of PPE Dstribution to Confirmed Cases in Western Massachusetts', fontsize=20)
    ax1 = axes[RATIOS.index(RATIO)].twinx()
    for ratio in RATIO:
        ax1.plot('Time', ratio, data=WM_CC, color=color[RATIO.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
        ax1.set_ylabel('Ratio', fontsize=15)
        ax1.legend(loc='best')
plt.show()

for category in categories:
    WM_CC['{} delta'.format(category)] = WM_CC[category].diff()
    WM_CC['Confirmed Cases delta'] = WM_CC['Confirmed Cases'].diff()

fig, axes = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes.flatten(), categories):
    sns.regplot('Confirmed Cases', category, data=WM_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig.delaxes(axes[1,2])
fig.suptitle('Regression Plot (Cumulative vs Cumulative)', fontsize=25)

fig1, axes1 = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes1.flatten(), categories):
    sns.regplot('Confirmed Cases delta', category + ' delta', data=WM_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig1.delaxes(axes1[1,2])
fig1.suptitle('Regression Plot (Delta vs Delta)', fontsize=25)

Text(0.5, 0.98, 'Regression Plot (Delta vs Delta)')

import statsmodels.api as sm
Y = [pd.DataFrame] * 5
X = [pd.DataFrame] * 5
for category in categories:
    Y[categories.index(category)] = WM_CC[category]
    X[categories.index(category)] = WM_CC['Confirmed Cases']
    X[categories.index(category)] = sm.add_constant(X[categories.index(category)])
    model = sm.OLS(Y[categories.index(category)], X[categories.index(category)])
    results = model.fit()
    print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:             N95s/KN95s   R-squared:                       0.608
Model:                            OLS   Adj. R-squared:                  0.586
Method:                 Least Squares   F-statistic:                     27.87
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           5.10e-05
Time:                        20:58:36   Log-Likelihood:                -280.74
No. Observations:                  20   AIC:                             565.5
Df Residuals:                      18   BIC:                             567.5
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.672e+06   4.41e+05     -3.792      0.001    -2.6e+06   -7.46e+05
Confirmed Cases   272.9084     51.697      5.279      0.000     164.296     381.520
==============================================================================
Omnibus:                       10.218   Durbin-Watson:                   0.211
Prob(Omnibus):                  0.006   Jarque-Bera (JB):                2.062
Skew:                          -0.066   Prob(JB):                        0.357
Kurtosis:                       1.433   Cond. No.                     5.28e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 5.28e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Masks   R-squared:                       0.647
Model:                            OLS   Adj. R-squared:                  0.627
Method:                 Least Squares   F-statistic:                     32.97
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           1.92e-05
Time:                        20:58:36   Log-Likelihood:                -281.98
No. Observations:                  20   AIC:                             568.0
Df Residuals:                      18   BIC:                             570.0
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.776e+06   4.69e+05     -3.785      0.001   -2.76e+06    -7.9e+05
Confirmed Cases   315.8850     55.015      5.742      0.000     200.303     431.467
==============================================================================
Omnibus:                       10.665   Durbin-Watson:                   0.203
Prob(Omnibus):                  0.005   Jarque-Bera (JB):                2.092
Skew:                          -0.056   Prob(JB):                        0.351
Kurtosis:                       1.420   Cond. No.                     5.28e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 5.28e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Gowns   R-squared:                       0.967
Model:                            OLS   Adj. R-squared:                  0.965
Method:                 Least Squares   F-statistic:                     521.6
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           9.63e-15
Time:                        20:58:36   Log-Likelihood:                -185.20
No. Observations:                  20   AIC:                             374.4
Df Residuals:                      18   BIC:                             376.4
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -2380.7629   3713.191     -0.641      0.529   -1.02e+04    5420.361
Confirmed Cases     9.9467      0.436     22.839      0.000       9.032      10.862
==============================================================================
Omnibus:                        8.528   Durbin-Watson:                   0.733
Prob(Omnibus):                  0.014   Jarque-Bera (JB):                1.954
Skew:                          -0.122   Prob(JB):                        0.376
Kurtosis:                       1.488   Cond. No.                     5.28e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 5.28e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Gloves   R-squared:                       0.988
Model:                            OLS   Adj. R-squared:                  0.988
Method:                 Least Squares   F-statistic:                     1525.
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           7.44e-19
Time:                        20:58:36   Log-Likelihood:                -209.86
No. Observations:                  20   AIC:                             423.7
Df Residuals:                      18   BIC:                             425.7
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const            2.637e+04   1.27e+04      2.069      0.053    -404.457    5.31e+04
Confirmed Cases    58.3715      1.494     39.058      0.000      55.232      61.511
==============================================================================
Omnibus:                        2.125   Durbin-Watson:                   1.057
Prob(Omnibus):                  0.346   Jarque-Bera (JB):                1.706
Skew:                           0.676   Prob(JB):                        0.426
Kurtosis:                       2.532   Cond. No.                     5.28e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 5.28e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:            Ventilators   R-squared:                        -inf
Model:                            OLS   Adj. R-squared:                   -inf
Method:                 Least Squares   F-statistic:                    -18.00
Date:                Sun, 13 Sep 2020   Prob (F-statistic):               1.00
Time:                        20:58:36   Log-Likelihood:                 601.21
No. Observations:                  20   AIC:                            -1198.
Df Residuals:                      18   BIC:                            -1196.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const              60.0000   3.11e-14   1.93e+15      0.000      60.000      60.000
Confirmed Cases  8.674e-19   3.65e-18      0.238      0.815    -6.8e-18    8.54e-18
==============================================================================
Omnibus:                      466.626   Durbin-Watson:                   0.000
Prob(Omnibus):                  0.000   Jarque-Bera (JB):                7.500
Skew:                           0.000   Prob(JB):                       0.0235
Kurtosis:                       0.000   Cond. No.                     5.28e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 5.28e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

Massachusetts¶

MA_CC = df.loc[df['Region']=='Massachusetts'].dropna()
categories = ['N95s/KN95s', 'Masks', 'Gowns', 'Gloves', 'Ventilators']
for category in categories:
    MA_CC['{} %'.format(category)] = MA_CC[category]/MA_CC[category].iloc[-1] * 100
    
# fig, axes = plt.subplots(2, 3, figsize=(25,15))
# for ax, category in zip(axes.flatten(), categories):
#     sns.distplot(MA_CC[category].diff(), ax=ax)
#     ax.set_xlabel(category, fontsize=15)
# fig.delaxes(axes[1,2])
MA_CC

# Confirmed cases vs PPE distribution
fig, axes = plt.subplots(2,1,figsize=(15,14))

percentages = ['N95s/KN95s %', 'Masks %', 'Gowns %', 'Gloves %', 'Ventilators %']

for category in categories:
    axes[0].plot('Time', category, data=MA_CC, marker='.', markersize=10)

axes[0].set_xlabel('Date', fontsize=15)
axes[0].set_ylabel('The Number of PPE Distribution', fontsize=15)
axes[0].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[0].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[0].legend()
axes[0].set_title('PPE Distribution vs Confirmed Cases in MA', fontsize=20)

ax1 = axes[0].twinx()
ax1.plot('Time', 'Confirmed Cases', data=MA_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax1.set_ylabel('Confirmed Cases', fontsize=15)
ax1.tick_params(direction='out', length=6, width=2, labelsize=10)
ax1.legend(loc='upper right')


for percentage in percentages:
    axes[1].plot('Time', percentage, data=MA_CC, marker='.', markersize=10)

axes[1].set_xlabel('Date', fontsize=15)
axes[1].set_ylabel('PPE Distribution %', fontsize=15)
axes[1].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
axes[1].tick_params(direction='out', length=6, width=2, labelsize=10)
axes[1].legend()
axes[1].set_title('PPE Distribution Percentage vs Confirmed Cases in MA', fontsize=20)

ax2 = axes[1].twinx()
ax2.plot('Time', 'Confirmed Cases', data=MA_CC, color='y', linestyle='--', linewidth=3, label='Confirmed Cases')

ax2.set_ylabel('Confirmed Cases', fontsize=15)
ax2.tick_params(direction='out', length=6, width=2, labelsize=10)
ax2.legend(loc='upper right')

plt.show()

pd.set_option("display.max_rows", None, "display.max_columns", None)


# Calculate the ratio of cumulative PPE data to cumulative confirmed cases data (Ratio 1)
for category in categories:
    MA_CC['{} Ratio 1'.format(category)] = MA_CC[category]/MA_CC['Confirmed Cases']
    
    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 2)
# (Interval=2 weeks, one week before + the week itself)
MA_CC['Confirmed Cases(ma1)'] = MA_CC['Confirmed Cases'].rolling(window=2,center=False).mean()
MA_CC.iloc[0,-1] = (42944+60265) / 2
for category in categories:
    MA_CC['{} Ratio 2'.format(category)] = MA_CC[category]/MA_CC['Confirmed Cases(ma1)']





# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 3)
# (Interval=2 weeks, one week after + the week itself)
MA_CC['Confirmed Cases(ma2)'] = MA_CC['Confirmed Cases'].rolling(window=2,center=False).mean().shift(-1)
for category in categories:
    MA_CC['{} Ratio 3'.format(category)] = MA_CC[category]/MA_CC['Confirmed Cases(ma2)']

    
    
# Calculate the ratio of cumulative PPE data to moving average of cumulative confirmed cases data (Ratio 4)
# (Interval=3 weeks, one week before + one week after + the week itself)
MA_CC['Confirmed Cases(ma3)'] = MA_CC['Confirmed Cases'].rolling(window=3,center=False).mean().shift(-1)
MA_CC.iloc[0,-1] = (42944+60265+72025)/3
for category in categories:
    MA_CC['{} Ratio 4'.format(category)] = MA_CC[category]/MA_CC['Confirmed Cases(ma3)']

    
    
# Calculate the ratio of delta PPE data to delta confirmed cases data (Ratio 5)
for category in categories:
    MA_CC['{} Ratio 5'.format(category)] = MA_CC[category].diff()/MA_CC['Confirmed Cases'].diff()
    
    
    
# Calculate the ratio of delta PPE data to moving average delta confirmed cases data (Ratio 6)
for category in categories:
    MA_CC['{} Ratio 6'.format(category)] = MA_CC[category].diff()/MA_CC['Confirmed Cases'].diff().rolling(window=2,center=False).mean()
MA_CC

# Plot the Ratio of PPE to Confirmed Cases

RATIO1 = []
RATIO2 = []
RATIO3 = []
RATIO4 = []
RATIO5 = []
RATIO6 = []
RATIOS = [RATIO1, RATIO2, RATIO3, RATIO4, RATIO5, RATIO6]
for RATIO in RATIOS:
    for category in categories:
        RATIO.append('{} Ratio {}'.format(category, RATIOS.index(RATIO)+1))

fig, axes = plt.subplots(6,1,figsize=(15,48))
color = ['b', 'g', 'r', 'c', 'm']


for RATIO in RATIOS:
    for category in categories:
        axes[RATIOS.index(RATIO)].plot('Time', category, data=MA_CC, color=color[categories.index(category)], linewidth=3, label=category, marker='.', markersize=12)
        axes[RATIOS.index(RATIO)].set_xlabel('Date', fontsize=15)
        axes[RATIOS.index(RATIO)].set_ylabel('The number of PPE', fontsize=15)
        axes[RATIOS.index(RATIO)].set_xlim([datetime.date(2020, 4, 29), datetime.date(2020,9,9)])
        axes[RATIOS.index(RATIO)].legend(loc='best')
        axes[RATIOS.index(RATIO)].set_title('The Ratio of PPE Dstribution to Confirmed Cases in Massachusetts', fontsize=20)
    ax1 = axes[RATIOS.index(RATIO)].twinx()
    for ratio in RATIO:
        ax1.plot('Time', ratio, data=MA_CC, color=color[RATIO.index(ratio)], linestyle='--', linewidth=3, label=ratio, marker='^', markersize=8)
        ax1.set_ylabel('Ratio', fontsize=15)
        ax1.legend(loc='best')
plt.show()

for category in categories:
    MA_CC['{} delta'.format(category)] = MA_CC[category].diff()
    MA_CC['Confirmed Cases delta'] = MA_CC['Confirmed Cases'].diff()

fig, axes = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes.flatten(), categories):
    sns.regplot('Confirmed Cases', category, data=MA_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig.delaxes(axes[1,2])
fig.suptitle('Regression Plot (Cumulative vs Cumulative)', fontsize=25)

fig1, axes1 = plt.subplots(2, 3, figsize=(25,15))

for ax, category in zip(axes1.flatten(), categories):
    sns.regplot('Confirmed Cases delta', category + ' delta', data=MA_CC, ax=ax)
    ax.set_xlabel('Confirmed Cases', fontsize=15)
    ax.set_ylabel(category, fontsize=15)
fig1.delaxes(axes1[1,2])
fig1.suptitle('Regression Plot (Delta vs Delta)', fontsize=25)

Text(0.5, 0.98, 'Regression Plot (Delta vs Delta)')

import statsmodels.api as sm
Y = [pd.DataFrame] * 5
X = [pd.DataFrame] * 5
for category in categories:
    Y[categories.index(category)] = MA_CC[category]
    X[categories.index(category)] = MA_CC['Confirmed Cases']
    X[categories.index(category)] = sm.add_constant(X[categories.index(category)])
    model = sm.OLS(Y[categories.index(category)], X[categories.index(category)])
    results = model.fit()
    print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:             N95s/KN95s   R-squared:                       0.683
Model:                            OLS   Adj. R-squared:                  0.665
Method:                 Least Squares   F-statistic:                     38.74
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           7.15e-06
Time:                        20:58:41   Log-Likelihood:                -320.47
No. Observations:                  20   AIC:                             644.9
Df Residuals:                      18   BIC:                             646.9
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.563e+07   3.45e+06     -4.526      0.000   -2.29e+07   -8.38e+06
Confirmed Cases   209.6131     33.679      6.224      0.000     138.855     280.371
==============================================================================
Omnibus:                        6.226   Durbin-Watson:                   0.248
Prob(Omnibus):                  0.044   Jarque-Bera (JB):                1.685
Skew:                           0.026   Prob(JB):                        0.431
Kurtosis:                       1.579   Cond. No.                     6.83e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.83e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Masks   R-squared:                       0.616
Model:                            OLS   Adj. R-squared:                  0.595
Method:                 Least Squares   F-statistic:                     28.89
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           4.16e-05
Time:                        20:58:41   Log-Likelihood:                -323.15
No. Observations:                  20   AIC:                             650.3
Df Residuals:                      18   BIC:                             652.3
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -1.456e+07   3.95e+06     -3.688      0.002   -2.29e+07   -6.27e+06
Confirmed Cases   207.0086     38.515      5.375      0.000     126.091     287.926
==============================================================================
Omnibus:                       13.542   Durbin-Watson:                   0.181
Prob(Omnibus):                  0.001   Jarque-Bera (JB):                2.274
Skew:                           0.025   Prob(JB):                        0.321
Kurtosis:                       1.349   Cond. No.                     6.83e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.83e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Gowns   R-squared:                       0.962
Model:                            OLS   Adj. R-squared:                  0.959
Method:                 Least Squares   F-statistic:                     450.9
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           3.42e-14
Time:                        20:58:41   Log-Likelihood:                -232.63
No. Observations:                  20   AIC:                             469.3
Df Residuals:                      18   BIC:                             471.3
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -2.655e+05   4.27e+04     -6.212      0.000   -3.55e+05   -1.76e+05
Confirmed Cases     8.8531      0.417     21.235      0.000       7.977       9.729
==============================================================================
Omnibus:                        3.706   Durbin-Watson:                   0.455
Prob(Omnibus):                  0.157   Jarque-Bera (JB):                2.332
Skew:                          -0.637   Prob(JB):                        0.312
Kurtosis:                       1.916   Cond. No.                     6.83e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.83e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                 Gloves   R-squared:                       0.962
Model:                            OLS   Adj. R-squared:                  0.960
Method:                 Least Squares   F-statistic:                     452.8
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           3.30e-14
Time:                        20:58:41   Log-Likelihood:                -284.41
No. Observations:                  20   AIC:                             572.8
Df Residuals:                      18   BIC:                             574.8
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const           -3.499e+06   5.69e+05     -6.146      0.000   -4.69e+06    -2.3e+06
Confirmed Cases   118.1383      5.552     21.279      0.000     106.474     129.803
==============================================================================
Omnibus:                        4.606   Durbin-Watson:                   0.384
Prob(Omnibus):                  0.100   Jarque-Bera (JB):                1.791
Skew:                          -0.344   Prob(JB):                        0.408
Kurtosis:                       1.705   Cond. No.                     6.83e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.83e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
                            OLS Regression Results                            
==============================================================================
Dep. Variable:            Ventilators   R-squared:                       0.709
Model:                            OLS   Adj. R-squared:                  0.693
Method:                 Least Squares   F-statistic:                     43.80
Date:                Sun, 13 Sep 2020   Prob (F-statistic):           3.26e-06
Time:                        20:58:41   Log-Likelihood:                -25.443
No. Observations:                  20   AIC:                             54.89
Df Residuals:                      18   BIC:                             56.88
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
const             669.3351      1.355    494.033      0.000     666.489     672.181
Confirmed Cases  8.745e-05   1.32e-05      6.618      0.000    5.97e-05       0.000
==============================================================================
Omnibus:                        1.915   Durbin-Watson:                   1.014
Prob(Omnibus):                  0.384   Jarque-Bera (JB):                1.205
Skew:                          -0.599   Prob(JB):                        0.547
Kurtosis:                       2.889   Cond. No.                     6.83e+05
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.83e+05. This might indicate that there are
strong multicollinearity or other numerical problems.

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases
0	2020-04-29	Central Massachusetts	138350.0	263767.0	43293.0	454250.0	79.0	5363.0
1	2020-04-30	Central Massachusetts	146820.0	269267.0	43573.0	457450.0	79.0	NaN
2	2020-05-01	Central Massachusetts	157800.0	276767.0	43838.0	472050.0	79.0	NaN
3	2020-05-02	Central Massachusetts	161960.0	277517.0	44658.0	480450.0	79.0	NaN
4	2020-05-03	Central Massachusetts	161960.0	277517.0	44658.0	480450.0	79.0	NaN
...	...	...	...	...	...	...	...	...
347	2020-08-12	Massachusetts	10476785.0	11665045.0	736612.0	10116833.0	679.0	113198.0
348	2020-08-19	Massachusetts	11161755.0	12488545.0	742512.0	10422377.0	679.0	115048.0
349	2020-08-26	Massachusetts	11192185.0	12525245.0	747887.0	10502349.0	679.0	117085.0
350	2020-09-02	Massachusetts	11194995.0	12544695.0	752587.0	10527949.0	679.0	119426.0
351	2020-09-09	Massachusetts	11206955.0	12547295.0	756087.0	10546549.0	679.0	121396.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %
176	2020-04-29	Southeastern Massachusetts	234696.000	408835.0	66317.0	686220.0	79.0	9446.0	17.127662	21.662026	42.409224	40.099902	100.0
183	2020-05-06	Southeastern Massachusetts	271607.000	494885.0	80542.0	853420.0	79.0	11616.0	19.821356	26.221365	51.506005	49.870389	100.0
190	2020-05-13	Southeastern Massachusetts	294853.000	579135.0	93570.0	1020308.0	79.0	13243.0	21.517804	30.685331	59.837313	59.622644	100.0
197	2020-05-20	Southeastern Massachusetts	306789.000	610735.0	101149.0	1111008.0	79.0	14953.0	22.388871	32.359650	64.684027	64.922783	100.0
204	2020-05-27	Southeastern Massachusetts	354094.000	643835.0	121174.0	1198208.0	79.0	16026.0	25.841098	34.113446	77.489864	70.018396	100.0
205	2020-06-03	Southeastern Massachusetts	371135.000	674335.0	127784.0	1338408.0	79.0	16832.0	27.084718	35.729481	81.716909	78.211113	100.0
206	2020-06-10	Southeastern Massachusetts	377062.000	691835.0	131684.0	1369208.0	79.0	17297.0	27.517259	36.656714	84.210930	80.010939	100.0
207	2020-06-17	Southeastern Massachusetts	378627.000	697785.0	134614.0	1370008.0	79.0	17597.0	27.631469	36.971974	86.084643	80.057688	100.0
208	2020-06-24	Southeastern Massachusetts	381785.000	705885.0	138104.0	1436408.0	79.0	17770.0	27.861934	37.401150	88.316472	83.937834	100.0
209	2020-07-01	Southeastern Massachusetts	383545.000	705885.0	139104.0	1453608.0	79.0	17965.0	27.990376	37.401150	88.955965	84.942931	100.0
210	2020-07-08	Southeastern Massachusetts	388295.000	713385.0	140304.0	1471908.0	79.0	18123.0	28.337021	37.798536	89.723356	86.012309	100.0
211	2020-07-15	Southeastern Massachusetts	402660.000	755335.0	145104.0	1519908.0	79.0	18280.0	29.385352	40.021247	92.792920	88.817233	100.0
212	2020-07-22	Southeastern Massachusetts	627550.000	1085285.0	146004.0	1536908.0	79.0	18501.0	45.797391	57.503570	93.368463	89.810644	100.0
213	2020-07-29	Southeastern Massachusetts	845815.000	1336835.0	149674.0	1572708.0	79.0	18803.0	61.725950	70.831887	95.715400	91.902650	100.0
214	2020-08-05	Southeastern Massachusetts	957085.000	1467935.0	151024.0	1596908.0	79.0	19143.0	69.846221	77.778190	96.578715	93.316800	100.0
215	2020-08-12	Southeastern Massachusetts	1273785.000	1783185.0	151924.0	1607908.0	79.0	19411.0	92.958377	94.481637	97.154258	93.959595	100.0
216	2020-08-19	Southeastern Massachusetts	1363775.000	1880035.0	152574.0	1665676.0	79.0	19648.0	99.525674	99.613211	97.569929	97.335322	100.0
217	2020-08-26	Southeastern Massachusetts	1369645.000	1885135.0	155474.0	1695476.0	79.0	19904.0	99.954055	99.883434	99.424457	99.076712	100.0
218	2020-09-02	Southeastern Massachusetts	1369795.000	1887235.0	155474.0	1703276.0	79.0	20316.0	99.965002	99.994702	99.424457	99.532513	100.0
219	2020-09-09	Southeastern Massachusetts	1370274.573	1887335.0	156374.0	1711276.0	79.0	20666.0	100.000000	100.000000	100.000000	100.000000	100.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %
132	2020-04-29	Northeastern Massachusetts	113720.000	226198.0	22974.0	335932.0	117.0	14920.0	8.791882	13.718007	40.468557	42.851093	100.0
139	2020-05-06	Northeastern Massachusetts	144600.000	260413.0	27849.0	427832.0	117.0	18056.0	11.179266	15.793010	49.055839	54.573749	100.0
146	2020-05-13	Northeastern Massachusetts	158983.000	297113.0	32024.0	514032.0	117.0	20158.0	12.291240	18.018719	56.410076	65.569321	100.0
153	2020-05-20	Northeastern Massachusetts	164193.000	320263.0	35174.0	559032.0	117.0	22088.0	12.694034	19.422674	61.958781	71.309468	100.0
160	2020-05-27	Northeastern Massachusetts	186775.000	339663.0	40604.0	614332.0	117.0	23370.0	14.439885	20.599207	71.523692	78.363471	100.0
161	2020-06-03	Northeastern Massachusetts	193712.000	366013.0	42635.0	688832.0	117.0	24334.0	14.976196	22.197229	75.101286	87.866604	100.0
162	2020-06-10	Northeastern Massachusetts	196071.000	375913.0	46315.0	700832.0	117.0	25003.0	15.158575	22.797625	81.583583	89.397310	100.0
163	2020-06-17	Northeastern Massachusetts	198897.000	385913.0	49195.0	703632.0	117.0	25424.0	15.377057	23.404085	86.656685	89.754475	100.0
164	2020-06-24	Northeastern Massachusetts	200936.000	390163.0	50520.0	726632.0	117.0	25734.0	15.534696	23.661831	88.990664	92.688328	100.0
165	2020-07-01	Northeastern Massachusetts	201506.000	391063.0	50870.0	730632.0	117.0	26022.0	15.578764	23.716412	89.607187	93.198563	100.0
166	2020-07-08	Northeastern Massachusetts	202526.000	392663.0	51370.0	731132.0	117.0	26316.0	15.657621	23.813446	90.487934	93.262343	100.0
167	2020-07-15	Northeastern Massachusetts	207526.000	442163.0	52670.0	739632.0	117.0	26659.0	16.044180	26.815423	92.777876	94.346593	100.0
168	2020-07-22	Northeastern Massachusetts	575436.000	857263.0	55670.0	744632.0	117.0	26969.0	44.487913	51.989583	98.062357	94.984387	100.0
169	2020-07-29	Northeastern Massachusetts	876266.000	1225163.0	55820.0	744632.0	117.0	27346.0	67.745580	74.301252	98.326581	94.984387	100.0
170	2020-08-05	Northeastern Massachusetts	891786.000	1242163.0	55920.0	746632.0	117.0	27934.0	68.945457	75.332234	98.502730	95.239504	100.0
171	2020-08-12	Northeastern Massachusetts	1119236.000	1473313.0	56270.0	761992.0	117.0	28416.0	86.529994	89.350560	99.119253	97.198808	100.0
172	2020-08-19	Northeastern Massachusetts	1289776.000	1643613.0	56470.0	771952.0	117.0	28909.0	99.714725	99.678576	99.471552	98.469294	100.0
173	2020-08-26	Northeastern Massachusetts	1292496.000	1643613.0	56720.0	783952.0	117.0	29416.0	99.925012	99.678576	99.911925	100.000000	100.0
174	2020-09-02	Northeastern Massachusetts	1292996.000	1648613.0	56770.0	783952.0	117.0	29972.0	99.963668	99.981806	100.000000	100.000000	100.0
175	2020-09-09	Northeastern Massachusetts	1293465.941	1648913.0	56770.0	783952.0	117.0	30541.0	100.000000	100.000000	100.000000	100.000000	100.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %	N95s/KN95s Ratio 1	Masks Ratio 1	Gowns Ratio 1	Gloves Ratio 1	Ventilators Ratio 1	Confirmed Cases(ma1)	N95s/KN95s Ratio 2	Masks Ratio 2	Gowns Ratio 2	Gloves Ratio 2	Ventilators Ratio 2	Confirmed Cases(ma2)	N95s/KN95s Ratio 3	Masks Ratio 3	Gowns Ratio 3	Gloves Ratio 3	Ventilators Ratio 3	Confirmed Cases(ma3)	N95s/KN95s Ratio 4	Masks Ratio 4	Gowns Ratio 4	Gloves Ratio 4	Ventilators Ratio 4	N95s/KN95s Ratio 5	Masks Ratio 5	Gowns Ratio 5	Gloves Ratio 5	Ventilators Ratio 5	N95s/KN95s Ratio 6	Masks Ratio 6	Gowns Ratio 6	Gloves Ratio 6	Ventilators Ratio 6
132	2020-04-29	Northeastern Massachusetts	113720.000	226198.0	22974.0	335932.0	117.0	14920.0	8.791882	13.718007	40.468557	42.851093	100.0	7.621984	15.160724	1.539812	22.515550	0.007842	12780.5	8.897930	17.698682	1.797582	26.284731	0.009155	16488.0	6.897137	13.718947	1.393377	20.374333	0.007096	14539.000000	7.821721	15.558016	1.580164	23.105578	0.008047	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
139	2020-05-06	Northeastern Massachusetts	144600.000	260413.0	27849.0	427832.0	117.0	18056.0	11.179266	15.793010	49.055839	54.573749	100.0	8.008418	14.422519	1.542368	23.694728	0.006480	16488.0	8.770015	15.794093	1.689047	25.948083	0.007096	19107.0	7.567907	13.629193	1.457529	22.391375	0.006123	17711.333333	8.164264	14.703184	1.572383	24.155832	0.006606	9.846939	10.910395	1.554528	29.304847	0.0	NaN	NaN	NaN	NaN	NaN
146	2020-05-13	Northeastern Massachusetts	158983.000	297113.0	32024.0	514032.0	117.0	20158.0	12.291240	18.018719	56.410076	65.569321	100.0	7.886844	14.739210	1.588650	25.500149	0.005804	19107.0	8.320668	15.549956	1.676035	26.902810	0.006123	21123.0	7.526535	14.065852	1.516073	24.335180	0.005539	20100.666667	7.909340	14.781251	1.593181	25.572883	0.005821	6.842531	17.459562	1.986204	41.008563	0.0	5.491791	14.012982	1.594120	32.913326	0.0
153	2020-05-20	Northeastern Massachusetts	164193.000	320263.0	35174.0	559032.0	117.0	22088.0	12.694034	19.422674	61.958781	71.309468	100.0	7.433584	14.499411	1.592448	25.309308	0.005297	21123.0	7.773186	15.161814	1.665199	26.465559	0.005539	22729.0	7.223943	14.090501	1.547538	24.595539	0.005148	21872.000000	7.506995	14.642602	1.608175	25.559254	0.005349	2.699482	11.994819	1.632124	23.316062	0.0	2.584325	11.483135	1.562500	22.321429	0.0
160	2020-05-27	Northeastern Massachusetts	186775.000	339663.0	40604.0	614332.0	117.0	23370.0	14.439885	20.599207	71.523692	78.363471	100.0	7.992084	14.534146	1.737441	26.287206	0.005006	22729.0	8.217475	14.944036	1.786440	27.028554	0.005148	23852.0	7.830580	14.240441	1.702331	25.755995	0.004905	23264.000000	8.028499	14.600370	1.745358	26.406981	0.005029	17.614665	15.132605	4.235569	43.135725	0.0	14.061021	12.079701	3.381071	34.433375	0.0
161	2020-06-03	Northeastern Massachusetts	193712.000	366013.0	42635.0	688832.0	117.0	24334.0	14.976196	22.197229	75.101286	87.866604	100.0	7.960549	15.041218	1.752075	28.307389	0.004808	23852.0	8.121415	15.345170	1.787481	28.879423	0.004905	24668.5	7.852606	14.837262	1.728317	27.923546	0.004743	24235.666667	7.992848	15.102246	1.759184	28.422243	0.004828	7.196058	27.334025	2.106846	77.282158	0.0	6.177204	23.463936	1.808549	66.340160	0.0
162	2020-06-10	Northeastern Massachusetts	196071.000	375913.0	46315.0	700832.0	117.0	25003.0	15.158575	22.797625	81.583583	89.397310	100.0	7.841899	15.034716	1.852378	28.029916	0.004679	24668.5	7.948234	15.238584	1.877496	28.409997	0.004743	25213.5	7.776429	14.909195	1.836913	27.795903	0.004640	24920.333333	7.867912	15.084590	1.858522	28.122898	0.004695	3.526158	14.798206	5.500747	17.937220	0.0	2.889161	12.124923	4.507042	14.696877	0.0
163	2020-06-17	Northeastern Massachusetts	198897.000	385913.0	49195.0	703632.0	117.0	25424.0	15.377057	23.404085	86.656685	89.754475	100.0	7.823199	15.179083	1.934983	27.675897	0.004602	25213.5	7.888512	15.305808	1.951137	27.906955	0.004640	25579.0	7.775793	15.087103	1.923257	27.508190	0.004574	25387.000000	7.834600	15.201205	1.937803	27.716233	0.004609	6.712589	23.752969	6.840855	6.650831	0.0	5.185321	18.348624	5.284404	5.137615	0.0
164	2020-06-24	Northeastern Massachusetts	200936.000	390163.0	50520.0	726632.0	117.0	25734.0	15.534696	23.661831	88.990664	92.688328	100.0	7.808191	15.161382	1.963162	28.236263	0.004547	25579.0	7.855506	15.253255	1.975058	28.407365	0.004574	25878.0	7.764742	15.077015	1.952237	28.079141	0.004521	25726.666667	7.810417	15.165704	1.963721	28.244312	0.004548	6.577419	13.709677	4.274194	74.193548	0.0	5.578659	11.627907	3.625171	62.927497	0.0
165	2020-07-01	Northeastern Massachusetts	201506.000	391063.0	50870.0	730632.0	117.0	26022.0	15.578764	23.716412	89.607187	93.198563	100.0	7.743678	15.028168	1.954884	28.077473	0.004496	25878.0	7.786769	15.111794	1.965762	28.233712	0.004521	26169.0	7.700180	14.943750	1.943903	27.919752	0.004471	26024.000000	7.743083	15.027014	1.954734	28.075315	0.004496	1.979167	3.125000	1.215278	13.888889	0.0	1.906355	3.010033	1.170569	13.377926	0.0
166	2020-07-08	Northeastern Massachusetts	202526.000	392663.0	51370.0	731132.0	117.0	26316.0	15.657621	23.813446	90.487934	93.262343	100.0	7.695926	14.921075	1.952044	27.782794	0.004446	26169.0	7.739157	15.004891	1.963010	27.938859	0.004471	26487.5	7.646097	14.824464	1.939405	27.602907	0.004417	26332.333333	7.691153	14.911819	1.950834	27.765561	0.004443	3.469388	5.442177	1.700680	1.700680	0.0	3.505155	5.498282	1.718213	1.718213	0.0
167	2020-07-15	Northeastern Massachusetts	207526.000	442163.0	52670.0	739632.0	117.0	26659.0	16.044180	26.815423	92.777876	94.346593	100.0	7.784463	16.585881	1.975693	27.744176	0.004389	26487.5	7.834866	16.693270	1.988485	27.923813	0.004417	26814.0	7.739464	16.490005	1.964272	27.583800	0.004363	26648.000000	7.787676	16.592727	1.976509	27.755629	0.004391	14.577259	144.314869	3.790087	24.781341	0.0	15.698587	155.416013	4.081633	26.687598	0.0
168	2020-07-22	Northeastern Massachusetts	575436.000	857263.0	55670.0	744632.0	117.0	26969.0	44.487913	51.989583	98.062357	94.984387	100.0	21.336942	31.786978	2.064222	27.610664	0.004338	26814.0	21.460282	31.970724	2.076154	27.770269	0.004363	27157.5	21.188843	31.566344	2.049894	27.419019	0.004308	26991.333333	21.319288	31.760676	2.062514	27.587818	0.004335	1186.806452	1339.032258	9.677419	16.129032	0.0	1126.830015	1271.362940	9.188361	15.313936	0.0
169	2020-07-29	Northeastern Massachusetts	876266.000	1225163.0	55820.0	744632.0	117.0	27346.0	67.745580	74.301252	98.326581	94.984387	100.0	32.043663	44.802275	2.041249	27.230015	0.004279	27157.5	32.266078	45.113247	2.055417	27.419019	0.004308	27640.0	31.702822	44.325724	2.019537	26.940376	0.004233	27416.333333	31.961458	44.687340	2.036013	27.160160	0.004268	797.957560	975.862069	0.397878	0.000000	0.0	875.778748	1071.033479	0.436681	0.000000	0.0
170	2020-08-05	Northeastern Massachusetts	891786.000	1242163.0	55920.0	746632.0	117.0	27934.0	68.945457	75.332234	98.502730	95.239504	100.0	31.924751	44.467781	2.001862	26.728431	0.004188	27640.0	32.264327	44.940774	2.023155	27.012735	0.004233	28175.0	31.651677	44.087418	1.984738	26.499805	0.004153	27898.666667	31.965184	44.524099	2.004397	26.762283	0.004194	26.394558	28.911565	0.170068	3.401361	0.0	32.165803	35.233161	0.207254	4.145078	0.0
171	2020-08-12	Northeastern Massachusetts	1119236.000	1473313.0	56270.0	761992.0	117.0	28416.0	86.529994	89.350560	99.119253	97.198808	100.0	39.387528	51.848008	1.980222	26.815597	0.004117	28175.0	39.724437	52.291500	1.997161	27.044969	0.004153	28662.5	39.048792	51.402111	1.963192	26.584980	0.004082	28419.666667	39.382446	51.841319	1.979967	26.812137	0.004117	471.887967	479.564315	0.726141	31.867220	0.0	425.140187	432.056075	0.654206	28.710280	0.0
172	2020-08-19	Northeastern Massachusetts	1289776.000	1643613.0	56470.0	771952.0	117.0	28909.0	99.714725	99.678576	99.471552	98.469294	100.0	44.615033	56.854717	1.953371	26.702826	0.004047	28662.5	44.998727	57.343672	1.970170	26.932473	0.004082	29162.5	44.227210	56.360497	1.936391	26.470707	0.004012	28913.666667	44.607833	56.845540	1.953056	26.698516	0.004047	345.922921	345.436105	0.405680	20.202840	0.0	349.825641	349.333333	0.410256	20.430769	0.0
173	2020-08-26	Northeastern Massachusetts	1292496.000	1643613.0	56720.0	783952.0	117.0	29416.0	99.925012	99.678576	99.911925	100.000000	100.0	43.938537	55.874796	1.928202	26.650530	0.003977	29162.5	44.320480	56.360497	1.944964	26.882195	0.004012	29694.0	43.527177	55.351687	1.910150	26.401024	0.003940	29432.333333	43.914153	55.843789	1.927132	26.635741	0.003975	5.364892	0.000000	0.493097	23.668639	0.0	5.440000	0.000000	0.500000	24.000000	0.0
174	2020-09-02	Northeastern Massachusetts	1292996.000	1648613.0	56770.0	783952.0	117.0	29972.0	99.963668	99.981806	100.000000	100.000000	100.0	43.140131	55.005105	1.894101	26.156146	0.003904	29694.0	43.544016	55.520071	1.911834	26.401024	0.003940	30256.5	42.734487	54.487895	1.876291	25.910201	0.003867	29976.333333	43.133895	54.997153	1.893827	26.152365	0.003903	0.899281	8.992806	0.089928	0.000000	0.0	0.940734	9.407338	0.094073	0.000000	0.0
175	2020-09-09	Northeastern Massachusetts	1293465.941	1648913.0	56770.0	783952.0	117.0	30541.0	100.000000	100.000000	100.000000	100.000000	100.0	42.351787	53.990144	1.858813	25.668839	0.003831	30256.5	42.750019	54.497810	1.876291	25.910201	0.003867	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.825907	0.527241	0.000000	0.000000	0.0	0.835451	0.533333	0.000000	0.000000	0.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %
88	2020-04-29	Metro West	212466.000	373412.0	50610.0	372180.0	136.0	12601.0	12.069462	17.771521	27.322640	25.838548	100.0
95	2020-05-06	Metro West	292073.000	475762.0	55812.0	592330.0	136.0	14825.0	16.591661	22.642589	30.131026	41.122434	100.0
102	2020-05-13	Metro West	336159.000	564732.0	71127.0	701630.0	136.0	16491.0	19.096035	26.876872	38.399080	48.710572	100.0
109	2020-05-20	Metro West	380619.000	610582.0	88237.0	870902.0	136.0	17755.0	21.621655	29.058977	47.636195	60.462259	100.0
116	2020-05-27	Metro West	449855.000	661482.0	128047.0	977602.0	136.0	18580.0	25.554713	31.481423	69.128278	67.869892	100.0
117	2020-06-03	Metro West	463396.000	687532.0	135759.0	1052774.0	136.0	19245.0	26.323931	32.721202	73.291728	73.088699	100.0
118	2020-06-10	Metro West	479836.000	722682.0	145989.0	1121074.0	136.0	19490.0	27.257831	34.394070	78.814561	77.830417	100.0
119	2020-06-17	Metro West	486648.000	724682.0	148709.0	1182274.0	136.0	19706.0	27.644797	34.489254	80.282998	82.079219	100.0
120	2020-06-24	Metro West	514245.000	730682.0	150139.0	1188574.0	136.0	19906.0	29.212488	34.774808	81.055007	82.516596	100.0
121	2020-07-01	Metro West	516985.000	737682.0	154039.0	1233574.0	136.0	20047.0	29.368137	35.107954	83.160486	85.640715	100.0
122	2020-07-08	Metro West	518680.000	737682.0	160914.0	1246574.0	136.0	20195.0	29.464425	35.107954	86.872068	86.543239	100.0
123	2020-07-15	Metro West	535320.000	783032.0	162441.0	1319174.0	136.0	20346.0	30.409686	37.266263	87.696444	91.583484	100.0
124	2020-07-22	Metro West	978560.000	1232432.0	163441.0	1319574.0	136.0	20589.0	55.588624	58.654224	88.236310	91.611254	100.0
125	2020-07-29	Metro West	1221330.000	1510082.0	169731.0	1358574.0	136.0	20880.0	69.379551	71.868215	91.632070	94.318824	100.0
126	2020-08-05	Metro West	1229780.000	1538182.0	172781.0	1368374.0	136.0	21274.0	69.859567	73.205558	93.278663	94.999188	100.0
127	2020-08-12	Metro West	1555340.000	1880582.0	173181.0	1379534.0	136.0	21530.0	88.353509	89.501147	93.494609	95.773969	100.0
128	2020-08-19	Metro West	1751980.000	2076382.0	177881.0	1397406.0	136.0	21831.0	99.523950	98.819712	96.031982	97.014731	100.0
129	2020-08-26	Metro West	1759100.000	2100882.0	180031.0	1431206.0	136.0	22179.0	99.928413	99.985722	97.192695	99.361291	100.0
130	2020-09-02	Metro West	1759850.000	2100982.0	184681.0	1439806.0	136.0	22598.0	99.971018	99.990482	99.703073	99.958345	100.0
131	2020-09-09	Metro West	1760360.188	2101182.0	185231.0	1440406.0	136.0	22949.0	100.000000	100.000000	100.000000	100.000000	100.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %
44	2020-04-29	Metro Boston	94235.0	130800.0	23036.0	465692.0	204.0	12539.0	24.432138	26.723874	42.571751	58.967612	100.0
51	2020-05-06	Metro Boston	115642.0	146600.0	27956.0	535892.0	204.0	14476.0	29.982292	29.951987	51.664172	67.856591	100.0
58	2020-05-13	Metro Boston	127621.0	187600.0	31366.0	575792.0	204.0	15587.0	33.088066	38.328736	57.966033	72.908874	100.0
65	2020-05-20	Metro Boston	135101.0	217400.0	37846.0	626992.0	204.0	16962.0	35.027392	44.417203	69.941417	79.392004	100.0
72	2020-05-27	Metro Boston	157158.0	234150.0	43206.0	646892.0	204.0	17596.0	40.746070	47.839412	79.846981	81.911814	100.0
73	2020-06-03	Metro Boston	159715.0	238150.0	46156.0	676892.0	204.0	18108.0	41.409019	48.656655	85.298738	85.710523	100.0
74	2020-06-10	Metro Boston	161838.0	242350.0	47216.0	673292.0	204.0	18398.0	41.959445	49.514761	87.257674	85.254678	100.0
75	2020-06-17	Metro Boston	163040.0	246350.0	48436.0	678892.0	204.0	18652.0	42.271086	50.332005	89.512299	85.963770	100.0
76	2020-06-24	Metro Boston	163736.0	251350.0	48511.0	691942.0	204.0	18805.0	42.451536	51.353560	89.650903	87.616209	100.0
77	2020-07-01	Metro Boston	165116.0	256850.0	49011.0	701342.0	204.0	18978.0	42.809326	52.477270	90.574929	88.806471	100.0
78	2020-07-08	Metro Boston	168236.0	259850.0	49511.0	716342.0	204.0	19186.0	43.618243	53.090203	91.498956	90.705825	100.0
79	2020-07-15	Metro Boston	179881.0	294100.0	51711.0	720342.0	204.0	19428.0	46.637421	60.087854	95.564673	91.212320	100.0
80	2020-07-22	Metro Boston	345331.0	467100.0	54111.0	720342.0	204.0	19637.0	89.533343	95.433650	100.000000	91.212320	100.0
81	2020-07-29	Metro Boston	370891.0	475350.0	54111.0	720342.0	204.0	19940.0	96.160238	97.119215	100.000000	91.212320	100.0
82	2020-08-05	Metro Boston	371961.0	478450.0	54111.0	722342.0	204.0	20448.0	96.437655	97.752579	100.000000	91.465567	100.0
83	2020-08-12	Metro Boston	371961.0	478450.0	54111.0	785742.0	204.0	20899.0	96.437655	97.752579	100.000000	99.493505	100.0
84	2020-08-19	Metro Boston	372901.0	478450.0	54111.0	785742.0	204.0	21393.0	96.681367	97.752579	100.000000	99.493505	100.0
85	2020-08-26	Metro Boston	384201.0	478450.0	54111.0	789742.0	204.0	21946.0	99.611098	97.752579	100.000000	100.000000	100.0
86	2020-09-02	Metro Boston	384701.0	489450.0	54111.0	789742.0	204.0	22508.0	99.740732	100.000000	100.000000	100.000000	100.0
87	2020-09-09	Metro Boston	385701.0	489450.0	54111.0	789742.0	204.0	22831.0	100.000000	100.000000	100.000000	100.000000	100.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %
220	2020-04-29	Western Massachusetts	156323.0	300642.0	50128.0	324630.0	60.0	5052.0	12.021684	18.292823	52.217211	53.049232	100.0
227	2020-05-06	Western Massachusetts	196135.0	356892.0	52813.0	366030.0	60.0	5773.0	15.083340	21.715403	55.014115	59.814590	100.0
234	2020-05-13	Western Massachusetts	217121.0	402897.0	58829.0	399030.0	60.0	6526.0	16.697223	24.514617	61.280847	65.207267	100.0
241	2020-05-20	Western Massachusetts	223347.0	435347.0	69279.0	433930.0	60.0	7164.0	17.176020	26.489066	72.166377	70.910431	100.0
248	2020-05-27	Western Massachusetts	259107.0	467597.0	73949.0	467930.0	60.0	7693.0	19.926066	28.451345	77.031011	76.466522	100.0
249	2020-06-03	Western Massachusetts	269912.0	483597.0	74919.0	484930.0	60.0	7982.0	20.757001	29.424879	78.041438	79.244568	100.0
250	2020-06-10	Western Massachusetts	274382.0	493747.0	77299.0	504643.0	60.0	8157.0	21.100757	30.042464	80.520630	82.465957	100.0
251	2020-06-17	Western Massachusetts	274972.0	496797.0	76569.0	499630.0	60.0	8281.0	21.146129	30.228044	79.760206	81.646760	100.0
252	2020-06-24	Western Massachusetts	275262.0	508247.0	83649.0	536043.0	60.0	8394.0	21.168431	30.924729	87.135283	87.597170	100.0
253	2020-07-01	Western Massachusetts	276162.0	512647.0	85099.0	540443.0	60.0	8549.0	21.237644	31.192451	88.645715	88.316194	100.0
254	2020-07-08	Western Massachusetts	276922.0	515447.0	87499.0	547443.0	60.0	8689.0	21.296090	31.362820	91.145741	89.460095	100.0
255	2020-07-15	Western Massachusetts	294047.0	566447.0	88599.0	548443.0	60.0	8844.0	22.613051	34.465959	92.291586	89.623509	100.0
256	2020-07-22	Western Massachusetts	626547.0	908947.0	90399.0	550943.0	60.0	9002.0	48.183247	55.305668	94.166606	90.032046	100.0
257	2020-07-29	Western Massachusetts	1076842.0	1377397.0	91849.0	557183.0	60.0	9207.0	82.812214	83.808915	95.677038	91.051752	100.0
258	2020-08-05	Western Massachusetts	1298047.0	1627297.0	93699.0	574481.0	60.0	9428.0	99.823508	99.014297	97.604142	93.878495	100.0
259	2020-08-12	Western Massachusetts	1298292.0	1630897.0	93699.0	587977.0	60.0	9587.0	99.842349	99.233342	97.604142	96.083936	100.0
260	2020-08-19	Western Massachusetts	1299942.0	1635997.0	93999.0	603241.0	60.0	9759.0	99.969239	99.543656	97.916645	98.578294	100.0
261	2020-08-26	Western Massachusetts	1300342.0	1640997.0	93999.0	603541.0	60.0	9904.0	100.000000	99.847885	97.916645	98.627319	100.0
262	2020-09-02	Western Massachusetts	1300342.0	1641497.0	93999.0	607941.0	60.0	10082.0	100.000000	99.878308	97.916645	99.346342	100.0
263	2020-09-09	Western Massachusetts	1300342.0	1643497.0	95999.0	611941.0	60.0	10212.0	100.000000	100.000000	100.000000	100.000000	100.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %
308	2020-04-29	Massachusetts	990826.0	1863412.0	285795.0	3987284.0	675.0	60265.0	8.841171	14.851105	37.799222	37.806528	99.410898
315	2020-05-06	Massachusetts	1246197.0	2290427.0	340057.0	5273534.0	675.0	72025.0	11.119854	18.254349	44.975909	50.002461	99.410898
322	2020-05-13	Massachusetts	1385499.0	2744148.0	411454.0	6083122.0	675.0	80497.0	12.362850	21.870435	54.418870	57.678791	99.410898
329	2020-05-20	Massachusetts	1481001.0	2951030.0	476763.0	6643694.0	675.0	88970.0	13.215017	23.519253	63.056632	62.994009	99.410898
336	2020-05-27	Massachusetts	1713615.0	3154780.0	576570.0	7097694.0	679.0	94220.0	15.290639	25.143109	76.257097	67.298734	100.000000
337	2020-06-03	Massachusetts	1784126.0	3290580.0	608770.0	7618366.0	679.0	97964.0	15.919811	26.225414	80.515867	72.235629	100.000000
338	2020-06-10	Massachusetts	2544530.0	3469230.0	638660.0	7888596.0	679.0	100158.0	22.704919	27.649226	84.469115	74.797889	100.000000
339	2020-06-17	Massachusetts	2556595.0	3434680.0	647490.0	7839666.0	679.0	101654.0	22.812575	27.373868	85.636970	74.333946	100.000000
340	2020-06-24	Massachusetts	4388525.0	3538480.0	666890.0	8271946.0	679.0	102762.0	39.158942	28.201138	88.202813	78.432727	100.000000
341	2020-07-01	Massachusetts	4409365.0	4007230.0	678510.0	8867546.0	679.0	103858.0	39.344898	31.937003	89.739673	84.080072	100.000000
342	2020-07-08	Massachusetts	4437140.0	4041230.0	691495.0	8922946.0	679.0	104961.0	39.592735	32.207978	91.457068	84.605362	100.000000
343	2020-07-15	Massachusetts	4530795.0	4689430.0	705322.0	9469546.0	679.0	106128.0	40.428421	37.374032	93.285826	89.788100	100.000000
344	2020-07-22	Massachusetts	7447255.0	6733830.0	714422.0	9590446.0	679.0	107413.0	66.452083	53.667583	94.489391	90.934447	100.000000
345	2020-07-29	Massachusetts	8923945.0	10001695.0	726012.0	9906079.0	679.0	109096.0	79.628632	79.711962	96.022283	93.927208	100.000000
346	2020-08-05	Massachusetts	9292070.0	10451295.0	734962.0	9971377.0	679.0	111371.0	82.913423	83.295204	97.206009	94.546349	100.000000
347	2020-08-12	Massachusetts	10476785.0	11665045.0	736612.0	10116833.0	679.0	113198.0	93.484671	92.968604	97.424238	95.925530	100.000000
348	2020-08-19	Massachusetts	11161755.0	12488545.0	742512.0	10422377.0	679.0	115048.0	99.596679	99.531772	98.204572	98.822629	100.000000
349	2020-08-26	Massachusetts	11192185.0	12525245.0	747887.0	10502349.0	679.0	117085.0	99.868207	99.824265	98.915469	99.580906	100.000000
350	2020-09-02	Massachusetts	11194995.0	12544695.0	752587.0	10527949.0	679.0	119426.0	99.893281	99.979278	99.537090	99.823639	100.000000
351	2020-09-09	Massachusetts	11206955.0	12547295.0	756087.0	10546549.0	679.0	121396.0	100.000000	100.000000	100.000000	100.000000	100.000000

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %	N95s/KN95s Ratio 1	Masks Ratio 1	Gowns Ratio 1	Gloves Ratio 1	Ventilators Ratio 1	Confirmed Cases(ma1)	N95s/KN95s Ratio 2	Masks Ratio 2	Gowns Ratio 2	Gloves Ratio 2	Ventilators Ratio 2	Confirmed Cases(ma2)	N95s/KN95s Ratio 3	Masks Ratio 3	Gowns Ratio 3	Gloves Ratio 3	Ventilators Ratio 3	Confirmed Cases(ma3)	N95s/KN95s Ratio 4	Masks Ratio 4	Gowns Ratio 4	Gloves Ratio 4	Ventilators Ratio 4	N95s/KN95s Ratio 5	Masks Ratio 5	Gowns Ratio 5	Gloves Ratio 5	Ventilators Ratio 5	N95s/KN95s Ratio 6	Masks Ratio 6	Gowns Ratio 6	Gloves Ratio 6	Ventilators Ratio 6
88	2020-04-29	Metro West	212466.000	373412.0	50610.0	372180.0	136.0	12601.0	12.069462	17.771521	27.322640	25.838548	100.0	16.861043	29.633521	4.016348	29.535751	0.010793	10910.0	19.474427	34.226581	4.638863	34.113657	0.012466	13713.0	15.493765	27.230511	3.690658	27.140669	0.009918	12215.000000	17.393860	30.569955	4.143266	30.469095	0.011134	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
95	2020-05-06	Metro West	292073.000	475762.0	55812.0	592330.0	136.0	14825.0	16.591661	22.642589	30.131026	41.122434	100.0	19.701383	32.091872	3.764722	39.954806	0.009174	13713.0	21.298986	34.694232	4.070007	43.194779	0.009918	15658.0	18.653276	30.384596	3.564440	37.829225	0.008686	14639.000000	19.951704	32.499624	3.812556	40.462463	0.009290	35.794514	46.020683	2.339029	98.988309	0.0	NaN	NaN	NaN	NaN	NaN
102	2020-05-13	Metro West	336159.000	564732.0	71127.0	701630.0	136.0	16491.0	19.096035	26.876872	38.399080	48.710572	100.0	20.384391	34.244861	4.313080	42.546237	0.008247	15658.0	21.468834	36.066675	4.542534	44.809682	0.008686	17123.0	19.632015	32.980903	4.153887	40.975880	0.007943	16357.000000	20.551385	34.525402	4.348414	42.894785	0.008314	26.462185	53.403361	9.192677	65.606242	0.0	22.666324	45.742931	7.874036	56.195373	0.0
109	2020-05-20	Metro West	380619.000	610582.0	88237.0	870902.0	136.0	17755.0	21.621655	29.058977	47.636195	60.462259	100.0	21.437285	34.389299	4.969699	49.051084	0.007660	17123.0	22.228523	35.658588	5.153127	50.861531	0.007943	18167.5	20.950544	33.608477	4.856860	47.937361	0.007486	17608.666667	21.615436	34.675084	5.010998	49.458714	0.007723	35.174051	36.273734	13.536392	133.917722	0.0	30.348123	31.296928	11.679181	115.544027	0.0
116	2020-05-27	Metro West	449855.000	661482.0	128047.0	977602.0	136.0	18580.0	25.554713	31.481423	69.128278	67.869892	100.0	24.211787	35.601830	6.891658	52.615823	0.007320	18167.5	24.761525	36.410183	7.048135	53.810486	0.007486	18912.5	23.786120	34.975915	6.770496	51.690787	0.007191	18526.666667	24.281486	35.704318	6.911497	52.767290	0.007341	83.922424	61.696970	48.254545	129.333333	0.0	66.286261	48.731450	38.113930	102.154141	0.0
117	2020-06-03	Metro West	463396.000	687532.0	135759.0	1052774.0	136.0	19245.0	26.323931	32.721202	73.291728	73.088699	100.0	24.078774	35.725227	7.054248	54.703767	0.007067	18912.5	24.502102	36.353311	7.178268	55.665512	0.007191	19367.5	23.926475	35.499264	7.009630	54.357764	0.007022	19105.000000	24.255221	35.987019	7.105941	55.104632	0.007119	20.362406	39.172932	11.596992	113.040602	0.0	18.175839	34.966443	10.351678	100.902013	0.0
118	2020-06-10	Metro West	479836.000	722682.0	145989.0	1121074.0	136.0	19490.0	27.257831	34.394070	78.814561	77.830417	100.0	24.619600	37.079631	7.490457	57.520472	0.006978	19367.5	24.775319	37.314160	7.537834	57.884291	0.007022	19598.0	24.483927	36.875293	7.449178	57.203490	0.006939	19480.333333	24.631817	37.098030	7.494174	57.549015	0.006981	67.102041	143.469388	41.755102	278.775510	0.0	36.131868	77.252747	22.483516	150.109890	0.0
119	2020-06-17	Metro West	486648.000	724682.0	148709.0	1182274.0	136.0	19706.0	27.644797	34.489254	80.282998	82.079219	100.0	24.695423	36.774688	7.546382	59.995636	0.006901	19598.0	24.831513	36.977345	7.587968	60.326258	0.006939	19806.0	24.570736	36.589013	7.508280	59.692719	0.006867	19700.666667	24.702108	36.784643	7.548425	60.011878	0.006903	31.537037	9.259259	12.592593	283.333333	0.0	29.553145	8.676790	11.800434	265.509761	0.0
120	2020-06-24	Metro West	514245.000	730682.0	150139.0	1188574.0	136.0	19906.0	29.212488	34.774808	81.055007	82.516596	100.0	25.833668	36.706621	7.542399	59.709334	0.006832	19806.0	25.964102	36.891952	7.580481	60.010805	0.006867	19976.5	25.742497	36.577078	7.515781	59.498611	0.006808	19886.333333	25.859217	36.742922	7.549858	59.768384	0.006839	137.985000	30.000000	7.150000	31.500000	0.0	132.677885	28.846154	6.875000	30.288462	0.0
121	2020-07-01	Metro West	516985.000	737682.0	154039.0	1233574.0	136.0	20047.0	29.368137	35.107954	83.160486	85.640715	100.0	25.788647	36.797626	7.683893	61.534095	0.006784	19976.5	25.879659	36.927490	7.711010	61.751258	0.006808	20121.0	25.693802	36.662293	7.655633	61.307788	0.006759	20049.333333	25.785645	36.793343	7.682999	61.526934	0.006783	19.432624	49.645390	27.659574	319.148936	0.0	16.070381	41.055718	22.873900	263.929619	0.0
122	2020-07-08	Metro West	518680.000	737682.0	160914.0	1246574.0	136.0	20195.0	29.464425	35.107954	86.872068	86.543239	100.0	25.683585	36.527952	7.968012	61.726863	0.006734	20121.0	25.778043	36.662293	7.997316	61.953879	0.006759	20270.5	25.587923	36.391900	7.938334	61.496954	0.006709	20196.000000	25.682313	36.526144	7.967617	61.723807	0.006734	11.452703	0.000000	46.452703	87.837838	0.0	11.730104	0.000000	47.577855	89.965398	0.0
123	2020-07-15	Metro West	535320.000	783032.0	162441.0	1319174.0	136.0	20346.0	30.409686	37.266263	87.696444	91.583484	100.0	26.310823	38.485796	7.983928	64.837020	0.006684	20270.5	26.408821	38.629141	8.013665	65.078513	0.006709	20467.5	26.154635	38.257335	7.936534	64.452131	0.006645	20376.666667	26.271225	38.427875	7.971912	64.739441	0.006674	110.198675	300.331126	10.112583	480.794702	0.0	111.304348	303.344482	10.214047	485.618729	0.0
124	2020-07-22	Metro West	978560.000	1232432.0	163441.0	1319574.0	136.0	20589.0	55.588624	58.654224	88.236310	91.611254	100.0	47.528292	59.858760	7.938268	64.091214	0.006605	20467.5	47.810431	60.214096	7.985391	64.471675	0.006645	20734.5	47.194772	59.438713	7.882563	63.641467	0.006559	20605.000000	47.491386	59.812279	7.932104	64.041446	0.006600	1824.032922	1849.382716	4.115226	1.646091	0.0	2249.949239	2281.218274	5.076142	2.030457	0.0
125	2020-07-29	Metro West	1221330.000	1510082.0	169731.0	1358574.0	136.0	20880.0	69.379551	71.868215	91.632070	94.318824	100.0	58.492816	72.321935	8.128879	65.065805	0.006513	20734.5	58.903277	72.829439	8.185922	65.522390	0.006559	21077.0	57.946102	71.645965	8.052901	64.457655	0.006453	20914.333333	58.396793	72.203210	8.115535	64.958991	0.006503	834.261168	954.123711	21.615120	134.020619	0.0	909.250936	1039.887640	23.558052	146.067416	0.0
126	2020-08-05	Metro West	1229780.000	1538182.0	172781.0	1368374.0	136.0	21274.0	69.859567	73.205558	93.278663	94.999188	100.0	57.806712	72.303375	8.121698	64.321425	0.006393	21077.0	58.347013	72.979172	8.197609	64.922617	0.006453	21402.0	57.460985	71.870947	8.073124	63.936735	0.006355	21228.000000	57.931977	72.460053	8.139297	64.460806	0.006407	21.446701	71.319797	7.741117	24.873096	0.0	24.671533	82.043796	8.905109	28.613139	0.0
127	2020-08-12	Metro West	1555340.000	1880582.0	173181.0	1379534.0	136.0	21530.0	88.353509	89.501147	93.494609	95.773969	100.0	72.240595	87.347051	8.043706	64.074965	0.006317	21402.0	72.672647	87.869451	8.091814	64.458181	0.006355	21680.5	71.739120	86.740712	7.987869	63.630175	0.006273	21545.000000	72.190299	87.286238	8.038106	64.030355	0.006312	1271.718750	1337.500000	1.562500	43.593750	0.0	1001.723077	1053.538462	1.230769	34.338462	0.0
128	2020-08-19	Metro West	1751980.000	2076382.0	177881.0	1397406.0	136.0	21831.0	99.523950	98.819712	96.031982	97.014731	100.0	80.251935	95.111630	8.148092	64.010169	0.006230	21680.5	80.809022	95.771869	8.204654	64.454510	0.006273	22005.0	79.617360	94.359555	8.083663	63.504022	0.006180	21846.666667	80.194385	95.043424	8.142249	63.964266	0.006225	653.289037	650.498339	15.614618	59.375415	0.0	706.068223	703.052065	16.876122	64.172352	0.0
129	2020-08-26	Metro West	1759100.000	2100882.0	180031.0	1431206.0	136.0	22179.0	99.928413	99.985722	97.192695	99.361291	100.0	79.313765	94.723928	8.117183	64.529780	0.006132	22005.0	79.940923	95.472938	8.181368	65.040036	0.006180	22388.5	78.571588	93.837551	8.041227	63.925944	0.006075	22202.666667	79.229222	94.622958	8.108531	64.460996	0.006125	20.459770	70.402299	6.178161	97.126437	0.0	21.941448	75.500770	6.625578	104.160247	0.0
130	2020-09-02	Metro West	1759850.000	2100982.0	184681.0	1439806.0	136.0	22598.0	99.971018	99.990482	99.703073	99.958345	100.0	77.876361	92.972033	8.172449	63.713868	0.006018	22388.5	78.605087	93.842017	8.248922	64.310070	0.006075	22773.5	77.276220	92.255560	8.109469	63.222869	0.005972	22575.333333	77.954552	93.065381	8.180654	63.777840	0.006024	1.789976	0.238663	11.097852	20.525060	0.0	1.955671	0.260756	12.125163	22.425033	0.0
131	2020-09-09	Metro West	1760360.188	2101182.0	185231.0	1440406.0	136.0	22949.0	100.000000	100.000000	100.000000	100.000000	100.0	76.707490	91.558761	8.071419	62.765524	0.005926	22773.5	77.298623	92.264342	8.133620	63.249215	0.005972	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.453527	0.569801	1.566952	1.709402	0.0	1.325164	0.519481	1.428571	1.558442	0.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %	N95s/KN95s Ratio 1	Masks Ratio 1	Gowns Ratio 1	Gloves Ratio 1	Ventilators Ratio 1	Confirmed Cases(ma1)	N95s/KN95s Ratio 2	Masks Ratio 2	Gowns Ratio 2	Gloves Ratio 2	Ventilators Ratio 2	Confirmed Cases(ma2)	N95s/KN95s Ratio 3	Masks Ratio 3	Gowns Ratio 3	Gloves Ratio 3	Ventilators Ratio 3	Confirmed Cases(ma3)	N95s/KN95s Ratio 4	Masks Ratio 4	Gowns Ratio 4	Gloves Ratio 4	Ventilators Ratio 4	N95s/KN95s Ratio 5	Masks Ratio 5	Gowns Ratio 5	Gloves Ratio 5	Ventilators Ratio 5	N95s/KN95s Ratio 6	Masks Ratio 6	Gowns Ratio 6	Gloves Ratio 6	Ventilators Ratio 6
44	2020-04-29	Metro Boston	94235.0	130800.0	23036.0	465692.0	204.0	12539.0	24.432138	26.723874	42.571751	58.967612	100.0	7.515352	10.431454	1.837148	37.139485	0.016269	10799.5	8.725867	12.111672	2.133062	43.121626	0.018890	13507.5	6.976495	9.683509	1.705423	34.476550	0.015103	12025.000000	7.836590	10.877339	1.915676	38.726985	0.016965	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
51	2020-05-06	Metro Boston	115642.0	146600.0	27956.0	535892.0	204.0	14476.0	29.982292	29.951987	51.664172	67.856591	100.0	7.988533	10.127107	1.931196	37.019342	0.014092	13507.5	8.561318	10.853230	2.069665	39.673663	0.015103	15031.5	7.693311	9.752852	1.859828	35.651266	0.013571	14200.666667	8.143420	10.323459	1.968640	37.737102	0.014366	11.051626	8.156944	2.540010	36.241611	0.0	NaN	NaN	NaN	NaN	NaN
58	2020-05-13	Metro Boston	127621.0	187600.0	31366.0	575792.0	204.0	15587.0	33.088066	38.328736	57.966033	72.908874	100.0	8.187656	12.035671	2.012318	36.940527	0.013088	15031.5	8.490237	12.480458	2.086685	38.305691	0.013571	16274.5	7.841777	11.527236	1.927310	35.380012	0.012535	15675.000000	8.141691	11.968102	2.001021	36.733142	0.013014	10.782178	36.903690	3.069307	35.913591	0.0	7.860236	26.902887	2.237533	26.181102	0.0
65	2020-05-20	Metro Boston	135101.0	217400.0	37846.0	626992.0	204.0	16962.0	35.027392	44.417203	69.941417	79.392004	100.0	7.964922	12.816885	2.231223	36.964509	0.012027	16274.5	8.301392	13.358321	2.325479	38.526038	0.012535	17279.0	7.818797	12.581747	2.190289	36.286359	0.011806	16715.000000	8.082620	13.006282	2.264194	37.510739	0.012205	5.440000	21.672727	4.712727	37.236364	0.0	6.017699	23.974256	5.213194	41.190668	0.0
72	2020-05-27	Metro Boston	157158.0	234150.0	43206.0	646892.0	204.0	17596.0	40.746070	47.839412	79.846981	81.911814	100.0	8.931462	13.307002	2.455444	36.763583	0.011594	17279.0	9.095318	13.551131	2.500492	37.438046	0.011806	17852.0	8.803383	13.116177	2.420233	36.236388	0.011427	17555.333333	8.952151	13.337827	2.461132	36.848745	0.011620	34.790221	26.419558	8.454259	31.388013	0.0	21.958188	16.674963	5.335988	19.810851	0.0
73	2020-06-03	Metro Boston	159715.0	238150.0	46156.0	676892.0	204.0	18108.0	41.409019	48.656655	85.298738	85.710523	100.0	8.820135	13.151646	2.548929	37.380826	0.011266	17852.0	8.946617	13.340242	2.585481	37.916872	0.011427	18253.0	8.750068	13.047170	2.528680	37.083877	0.011176	18034.000000	8.856327	13.205612	2.559388	37.534213	0.011312	4.994141	7.812500	5.761719	58.593750	0.0	4.462478	6.980803	5.148342	52.356021	0.0
74	2020-06-10	Metro Boston	161838.0	242350.0	47216.0	673292.0	204.0	18398.0	41.959445	49.514761	87.257674	85.254678	100.0	8.796500	13.172627	2.566366	36.595934	0.011088	18253.0	8.866378	13.277269	2.586753	36.886649	0.011176	18525.0	8.736194	13.082321	2.548772	36.345047	0.011012	18386.000000	8.802241	13.181225	2.568041	36.619819	0.011095	7.320690	14.482759	3.655172	-12.413793	0.0	5.294264	10.473815	2.643392	-8.977556	0.0
75	2020-06-17	Metro Boston	163040.0	246350.0	48436.0	678892.0	204.0	18652.0	42.271086	50.332005	89.512299	85.963770	100.0	8.741154	13.207699	2.596826	36.397813	0.010937	18525.0	8.801080	13.298246	2.614629	36.647341	0.011012	18728.5	8.705449	13.153750	2.586219	36.249139	0.010892	18618.333333	8.756960	13.231582	2.601522	36.463629	0.010957	4.732283	15.748031	4.803150	22.047244	0.0	4.419118	14.705882	4.485294	20.588235	0.0
76	2020-06-24	Metro Boston	163736.0	251350.0	48511.0	691942.0	204.0	18805.0	42.451536	51.353560	89.650903	87.616209	100.0	8.707046	13.366126	2.579686	36.795639	0.010848	18728.5	8.742612	13.420722	2.590223	36.945938	0.010892	18891.5	8.667178	13.304925	2.567874	36.627160	0.010799	18811.666667	8.703960	13.361389	2.578772	36.782599	0.010844	4.549020	32.679739	0.490196	85.294118	0.0	3.420147	24.570025	0.368550	64.127764	0.0
77	2020-07-01	Metro Boston	165116.0	256850.0	49011.0	701342.0	204.0	18978.0	42.809326	52.477270	90.574929	88.806471	100.0	8.700390	13.534092	2.582517	36.955527	0.010749	18891.5	8.740227	13.596062	2.594341	37.124739	0.010799	19082.0	8.652971	13.460329	2.568441	36.754114	0.010691	18989.666667	8.695045	13.525777	2.580930	36.932823	0.010743	7.976879	31.791908	2.890173	54.335260	0.0	8.466258	33.742331	3.067485	57.668712	0.0
78	2020-07-08	Metro Boston	168236.0	259850.0	49511.0	716342.0	204.0	19186.0	43.618243	53.090203	91.498956	90.705825	100.0	8.768685	13.543730	2.580580	37.336704	0.010633	19082.0	8.816476	13.617545	2.594644	37.540195	0.010691	19307.0	8.713731	13.458849	2.564407	37.102709	0.010566	19197.333333	8.763509	13.535734	2.579056	37.314662	0.010626	15.000000	14.423077	2.403846	72.115385	0.0	16.377953	15.748031	2.624672	78.740157	0.0
79	2020-07-15	Metro Boston	179881.0	294100.0	51711.0	720342.0	204.0	19428.0	46.637421	60.087854	95.564673	91.212320	100.0	9.258853	15.137945	2.661674	37.077517	0.010500	19307.0	9.316880	15.232817	2.678355	37.309888	0.010566	19532.5	9.209318	15.056956	2.647434	36.879150	0.010444	19417.000000	9.264098	15.146521	2.663182	37.098522	0.010506	48.119835	141.528926	9.090909	16.528926	0.0	51.755556	152.222222	9.777778	17.777778	0.0
80	2020-07-22	Metro Boston	345331.0	467100.0	54111.0	720342.0	204.0	19637.0	89.533343	95.433650	100.000000	91.212320	100.0	17.585731	23.786729	2.755563	36.682895	0.010389	19532.5	17.679816	23.913990	2.770306	36.879150	0.010444	19788.5	17.451095	23.604619	2.734467	36.402052	0.010309	19668.333333	17.557715	23.748835	2.751174	36.624456	0.010372	791.626794	827.751196	11.483254	0.000000	0.0	733.702882	767.184035	10.643016	0.000000	0.0
81	2020-07-29	Metro Boston	370891.0	475350.0	54111.0	720342.0	204.0	19940.0	96.160238	97.119215	100.000000	91.212320	100.0	18.600351	23.839017	2.713691	36.125476	0.010231	19788.5	18.742755	24.021528	2.734467	36.402052	0.010309	20194.0	18.366396	23.539170	2.679558	35.671090	0.010102	20008.333333	18.536826	23.757601	2.704423	36.002099	0.010196	84.356436	27.227723	0.000000	0.000000	0.0	99.843750	32.226562	0.000000	0.000000	0.0
82	2020-08-05	Metro Boston	371961.0	478450.0	54111.0	722342.0	204.0	20448.0	96.437655	97.752579	100.000000	91.465567	100.0	18.190581	23.398376	2.646273	35.325802	0.009977	20194.0	18.419382	23.692681	2.679558	35.770130	0.010102	20673.5	17.992164	23.143154	2.617409	34.940479	0.009868	20429.000000	18.207499	23.420138	2.648735	35.358657	0.009986	2.106299	6.102362	0.000000	3.937008	0.0	2.638718	7.644883	0.000000	4.932182	0.0
83	2020-08-12	Metro Boston	371961.0	478450.0	54111.0	785742.0	204.0	20899.0	96.437655	97.752579	100.000000	99.493505	100.0	17.798029	22.893440	2.589167	37.597110	0.009761	20673.5	17.992164	23.143154	2.617409	38.007207	0.009868	21146.0	17.590135	22.626029	2.558924	37.157949	0.009647	20913.333333	17.785830	22.877749	2.587392	37.571342	0.009755	0.000000	0.000000	0.000000	140.576497	0.0	0.000000	0.000000	0.000000	132.221064	0.0
84	2020-08-19	Metro Boston	372901.0	478450.0	54111.0	785742.0	204.0	21393.0	96.681367	97.752579	100.000000	99.493505	100.0	17.430982	22.364792	2.529379	36.728930	0.009536	21146.0	17.634588	22.626029	2.558924	37.157949	0.009647	21669.5	17.208565	22.079420	2.497104	36.260274	0.009414	21412.666667	17.414972	22.344251	2.527056	36.695196	0.009527	1.902834	0.000000	0.000000	0.000000	0.0	1.989418	0.000000	0.000000	0.000000	0.0
85	2020-08-26	Metro Boston	384201.0	478450.0	54111.0	789742.0	204.0	21946.0	99.611098	97.752579	100.000000	100.000000	100.0	17.506653	21.801239	2.465643	35.985692	0.009296	21669.5	17.730035	22.079420	2.497104	36.444865	0.009414	22227.0	17.285329	21.525622	2.434472	35.530751	0.009178	21949.000000	17.504260	21.798260	2.465306	35.980774	0.009294	20.433996	0.000000	0.000000	7.233273	0.0	21.585482	0.000000	0.000000	7.640879	0.0
86	2020-09-02	Metro Boston	384701.0	489450.0	54111.0	789742.0	204.0	22508.0	99.740732	100.000000	100.000000	100.000000	100.0	17.091745	21.745602	2.404079	35.087169	0.009063	22227.0	17.307824	22.020516	2.434472	35.530751	0.009178	22669.5	16.969982	21.590684	2.386952	34.837204	0.008999	22428.333333	17.152456	21.822843	2.412618	35.211801	0.009096	0.889680	19.572954	0.000000	0.000000	0.0	0.896861	19.730942	0.000000	0.000000	0.0
87	2020-09-09	Metro Boston	385701.0	489450.0	54111.0	789742.0	204.0	22831.0	100.000000	100.000000	100.000000	100.000000	100.0	16.893741	21.437957	2.370067	34.590776	0.008935	22669.5	17.014094	21.590684	2.386952	34.837204	0.008999	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.095975	0.000000	0.000000	0.000000	0.0	2.259887	0.000000	0.000000	0.000000	0.0

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %
0	2020-04-29	Central Massachusetts	138350.0	263767.0	43293.0	454250.0	79.0	5363.0	12.562894	19.186306	45.118494	41.951345	95.180723
7	2020-05-06	Central Massachusetts	170144.0	292767.0	46627.0	794650.0	79.0	7056.0	15.449953	21.295754	48.593076	73.388302	95.180723
14	2020-05-13	Central Massachusetts	184220.0	350367.0	54390.0	858250.0	79.0	8335.0	16.728127	25.485555	56.683411	79.261952	95.180723
21	2020-05-20	Central Massachusetts	191280.0	364617.0	60380.0	889350.0	79.0	9857.0	17.369211	26.522094	62.925985	82.134130	95.180723
28	2020-05-27	Central Massachusetts	220534.0	392367.0	69922.0	905950.0	83.0	10743.0	20.025625	28.540618	72.870334	83.667189	100.000000
29	2020-06-03	Central Massachusetts	239254.0	420667.0	75489.0	975650.0	83.0	11296.0	21.725498	30.599149	78.672072	90.104193	100.000000
30	2020-06-10	Central Massachusetts	246659.0	456767.0	79129.0	986150.0	83.0	11641.0	22.397910	33.225048	82.465556	91.073899	100.000000
31	2020-06-17	Central Massachusetts	245449.0	462867.0	83339.0	1003150.0	83.0	11827.0	22.288036	33.668760	86.853075	92.643900	100.000000
32	2020-06-24	Central Massachusetts	246509.0	465367.0	84339.0	1007650.0	83.0	12001.0	22.384289	33.850609	87.895241	93.059488	100.000000
33	2020-07-01	Central Massachusetts	248049.0	472067.0	88739.0	1013850.0	83.0	12148.0	22.524129	34.337964	92.480772	93.632077	100.000000
34	2020-07-08	Central Massachusetts	249229.0	474167.0	90249.0	1015450.0	83.0	12296.0	22.631279	34.490717	94.054443	93.779842	100.000000
35	2020-07-15	Central Massachusetts	254659.0	493317.0	93149.0	1026050.0	83.0	12409.0	23.124351	35.883681	97.076724	94.758783	100.000000
36	2020-07-22	Central Massachusetts	425969.0	687317.0	93149.0	1027050.0	83.0	12541.0	38.680183	49.995163	97.076724	94.851136	100.000000
37	2020-07-29	Central Massachusetts	546539.0	814117.0	93179.0	1027650.0	83.0	12726.0	49.628561	59.218544	97.107989	94.906548	100.000000
38	2020-08-05	Central Massachusetts	549419.0	820117.0	95779.0	1037650.0	83.0	12968.0	49.890080	59.654982	99.817621	95.830078	100.000000
39	2020-08-12	Central Massachusetts	864179.0	1141467.0	95779.0	1067250.0	83.0	13138.0	78.471913	83.029852	99.817621	98.563726	100.000000
40	2020-08-19	Central Massachusetts	1088389.0	1371917.0	95829.0	1071930.0	83.0	13317.0	98.831338	99.792692	99.869729	98.995938	100.000000
41	2020-08-26	Central Massachusetts	1091409.0	1374017.0	95904.0	1072002.0	83.0	13513.0	99.105569	99.945445	99.947892	99.002588	100.000000
42	2020-09-02	Central Massachusetts	1091759.0	1374767.0	95904.0	1076802.0	83.0	13705.0	99.137351	100.000000	99.947892	99.445882	100.000000
43	2020-09-09	Central Massachusetts	1101259.0	1374767.0	95954.0	1082802.0	83.0	13906.0	100.000000	100.000000	100.000000	100.000000	100.000000

	Time	Region	N95s/KN95s	Masks	Gowns	Gloves	Ventilators	Confirmed Cases	N95s/KN95s %	Masks %	Gowns %	Gloves %	Ventilators %	N95s/KN95s Ratio 1	Masks Ratio 1	Gowns Ratio 1	Gloves Ratio 1	Ventilators Ratio 1	Confirmed Cases(ma1)	N95s/KN95s Ratio 2	Masks Ratio 2	Gowns Ratio 2	Gloves Ratio 2	Ventilators Ratio 2	Confirmed Cases(ma2)	N95s/KN95s Ratio 3	Masks Ratio 3	Gowns Ratio 3	Gloves Ratio 3	Ventilators Ratio 3	Confirmed Cases(ma3)	N95s/KN95s Ratio 4	Masks Ratio 4	Gowns Ratio 4	Gloves Ratio 4	Ventilators Ratio 4	N95s/KN95s Ratio 5	Masks Ratio 5	Gowns Ratio 5	Gloves Ratio 5	Ventilators Ratio 5	N95s/KN95s Ratio 6	Masks Ratio 6	Gowns Ratio 6	Gloves Ratio 6	Ventilators Ratio 6
0	2020-04-29	Central Massachusetts	138350.0	263767.0	43293.0	454250.0	79.0	5363.0	12.562894	19.186306	45.118494	41.951345	95.180723	25.797128	49.182734	8.072534	84.700727	0.014731	4418.5	31.311531	59.696051	9.798122	102.806382	0.017879	6209.5	22.280377	42.477977	6.972059	73.154038	0.012722	5297.666667	26.115271	49.789278	8.172088	85.745297	0.014912	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	2020-05-06	Central Massachusetts	170144.0	292767.0	46627.0	794650.0	79.0	7056.0	15.449953	21.295754	48.593076	73.388302	95.180723	24.113379	41.491922	6.608135	112.620465	0.011196	6209.5	27.400596	47.148241	7.508978	127.973267	0.012722	7695.5	22.109545	38.043922	6.058996	103.261646	0.010266	6918.000000	24.594391	42.319601	6.739954	114.867014	0.011419	18.779681	17.129356	1.969285	201.063201	0.000000	NaN	NaN	NaN	NaN	NaN
14	2020-05-13	Central Massachusetts	184220.0	350367.0	54390.0	858250.0	79.0	8335.0	16.728127	25.485555	56.683411	79.261952	95.180723	22.101980	42.035633	6.525495	102.969406	0.009478	7695.5	23.938665	45.528816	7.067767	111.526217	0.010266	9096.0	20.252858	38.518799	5.979551	94.354661	0.008685	8416.000000	21.889259	41.631060	6.462690	101.978375	0.009387	11.005473	45.035184	6.069586	49.726349	0.000000	9.472409	38.761777	5.224092	42.799462	0.000000
21	2020-05-20	Central Massachusetts	191280.0	364617.0	60380.0	889350.0	79.0	9857.0	17.369211	26.522094	62.925985	82.134130	95.180723	19.405499	36.990667	6.125596	90.225221	0.008015	9096.0	21.029024	40.085422	6.638083	97.773747	0.008685	10300.0	18.570874	35.399709	5.862136	86.344660	0.007670	9645.000000	19.832037	37.803733	6.260238	92.208398	0.008191	4.638633	9.362681	3.935611	20.433640	0.000000	5.041057	10.174938	4.277044	22.206355	0.000000
28	2020-05-27	Central Massachusetts	220534.0	392367.0	69922.0	905950.0	83.0	10743.0	20.025625	28.540618	72.870334	83.667189	100.000000	20.528158	36.523038	6.508610	84.329331	0.007726	10300.0	21.411068	38.093883	6.788544	87.956311	0.008058	11019.5	20.013068	35.606606	6.345297	82.213349	0.007532	10632.000000	20.742476	36.904345	6.576561	85.209744	0.007807	33.018059	31.320542	10.769752	18.735892	0.004515	24.297342	23.048173	7.925249	13.787375	0.003322
29	2020-06-03	Central Massachusetts	239254.0	420667.0	75489.0	975650.0	83.0	11296.0	21.725498	30.599149	78.672072	90.104193	100.000000	21.180418	37.240351	6.682808	86.371282	0.007348	11019.5	21.711874	38.174781	6.850492	88.538500	0.007532	11468.5	20.861839	36.680211	6.582291	85.072154	0.007237	11226.666667	21.311223	37.470338	6.724080	86.904691	0.007393	33.851718	51.175407	10.066908	126.039783	0.000000	26.018068	39.332870	7.737318	96.872828	0.000000
30	2020-06-10	Central Massachusetts	246659.0	456767.0	79129.0	986150.0	83.0	11641.0	22.397910	33.225048	82.465556	91.073899	100.000000	21.188815	39.237780	6.797440	84.713513	0.007130	11468.5	21.507521	39.827964	6.899682	85.987705	0.007237	11734.0	21.020879	38.926794	6.743566	84.042100	0.007073	11588.000000	21.285727	39.417242	6.828530	85.100967	0.007163	21.463768	104.637681	10.550725	30.434783	0.000000	16.492205	80.400891	8.106904	23.385301	0.000000
31	2020-06-17	Central Massachusetts	245449.0	462867.0	83339.0	1003150.0	83.0	11827.0	22.288036	33.668760	86.853075	92.643900	100.000000	20.753276	39.136467	7.046504	84.818635	0.007018	11734.0	20.917760	39.446651	7.102352	85.490881	0.007073	11914.0	20.601729	38.850680	6.995048	84.199261	0.006967	11823.000000	20.760298	39.149708	7.048888	84.847331	0.007020	-6.505376	32.795699	22.634409	91.397849	0.000000	-4.557439	22.975518	15.856874	64.030132	0.000000
32	2020-06-24	Central Massachusetts	246509.0	465367.0	84339.0	1007650.0	83.0	12001.0	22.384289	33.850609	87.895241	93.059488	100.000000	20.540705	38.777352	7.027664	83.963836	0.006916	11914.0	20.690700	39.060517	7.078983	84.576968	0.006967	12074.5	20.415669	38.541306	6.984886	83.452731	0.006874	11992.000000	20.556121	38.806454	7.032939	84.026851	0.006921	6.091954	14.367816	5.747126	25.862069	0.000000	5.888889	13.888889	5.555556	25.000000	0.000000
33	2020-07-01	Central Massachusetts	248049.0	472067.0	88739.0	1013850.0	83.0	12148.0	22.524129	34.337964	92.480772	93.632077	100.000000	20.418917	38.859648	7.304824	83.458182	0.006832	12074.5	20.543211	39.096194	7.349290	83.966210	0.006874	12222.0	20.295287	38.624366	7.260596	82.952872	0.006791	12148.333333	20.418356	38.858581	7.304623	83.455892	0.006832	10.476190	45.578231	29.931973	42.176871	0.000000	9.595016	41.744548	27.414330	38.629283	0.000000
34	2020-07-08	Central Massachusetts	249229.0	474167.0	90249.0	1015450.0	83.0	12296.0	22.631279	34.490717	94.054443	93.779842	100.000000	20.269112	38.562703	7.339704	82.583767	0.006750	12222.0	20.391834	38.796187	7.384143	83.083783	0.006791	12352.5	20.176402	38.386319	7.306132	82.206031	0.006719	12284.333333	20.288362	38.599327	7.346675	82.662198	0.006757	7.972973	14.189189	10.202703	10.810811	0.000000	8.000000	14.237288	10.237288	10.847458	0.000000
35	2020-07-15	Central Massachusetts	254659.0	493317.0	93149.0	1026050.0	83.0	12409.0	23.124351	35.883681	97.076724	94.758783	100.000000	20.522121	39.754775	7.506568	82.685954	0.006689	12352.5	20.615989	39.936612	7.540903	83.064157	0.006719	12475.0	20.413547	39.544449	7.466854	82.248497	0.006653	12415.333333	20.511652	39.734495	7.502739	82.643774	0.006685	48.053097	169.469027	25.663717	93.805310	0.000000	41.609195	146.743295	22.222222	81.226054	0.000000
36	2020-07-22	Central Massachusetts	425969.0	687317.0	93149.0	1027050.0	83.0	12541.0	38.680183	49.995163	97.076724	94.851136	100.000000	33.966111	54.805598	7.427558	81.895383	0.006618	12475.0	34.145812	55.095551	7.466854	82.328657	0.006653	12633.5	33.717418	54.404322	7.373174	81.295761	0.006570	12558.666667	33.918330	54.728501	7.417109	81.780178	0.006609	1297.803030	1469.696970	0.000000	7.575758	0.000000	1398.448980	1583.673469	0.000000	8.163265	0.000000
37	2020-07-29	Central Massachusetts	546539.0	814117.0	93179.0	1027650.0	83.0	12726.0	49.628561	59.218544	97.107989	94.906548	100.000000	42.946645	63.972733	7.321939	80.752004	0.006522	12633.5	43.261092	64.441129	7.375549	81.343254	0.006570	12847.0	42.542150	63.370203	7.252977	79.991438	0.006461	12745.000000	42.882621	63.877364	7.311024	80.631620	0.006512	651.729730	685.405405	0.162162	3.243243	0.000000	760.694006	800.000000	0.189274	3.785489	0.000000
38	2020-08-05	Central Massachusetts	549419.0	820117.0	95779.0	1037650.0	83.0	12968.0	49.890080	59.654982	99.817621	95.830078	100.000000	42.367289	63.241595	7.385796	80.016194	0.006400	12847.0	42.766327	63.837238	7.455359	80.769830	0.006461	13053.0	42.091397	62.829771	7.337700	79.495135	0.006359	12944.000000	42.445844	63.358854	7.399490	80.164555	0.006412	11.900826	24.793388	10.743802	41.322314	0.000000	13.489461	28.103044	12.177986	46.838407	0.000000
39	2020-08-12	Central Massachusetts	864179.0	1141467.0	95779.0	1067250.0	83.0	13138.0	78.471913	83.029852	99.817621	98.563726	100.000000	65.777059	86.882859	7.290227	81.233826	0.006318	13053.0	66.205393	87.448632	7.337700	81.762813	0.006359	13227.5	65.331998	86.294991	7.240900	80.684181	0.006275	13141.000000	65.762042	86.863024	7.288563	81.215280	0.006316	1851.529412	1890.294118	0.000000	174.117647	0.000000	1527.961165	1559.951456	0.000000	143.689320	0.000000
40	2020-08-19	Central Massachusetts	1088389.0	1371917.0	95829.0	1071930.0	83.0	13317.0	98.831338	99.792692	99.869729	98.995938	100.000000	81.729293	103.019974	7.195990	80.493354	0.006233	13227.5	82.282291	103.717029	7.244680	81.037989	0.006275	13415.0	81.132240	102.267387	7.143422	79.905330	0.006187	13322.666667	81.694531	102.976156	7.192929	80.459117	0.006230	1252.569832	1287.430168	0.279330	26.145251	0.000000	1284.871060	1320.630372	0.286533	26.819484	0.000000
41	2020-08-26	Central Massachusetts	1091409.0	1374017.0	95904.0	1072002.0	83.0	13513.0	99.105569	99.945445	99.947892	99.002588	100.000000	80.767335	101.681122	7.097166	79.331163	0.006142	13415.0	81.357361	102.423928	7.149012	79.910697	0.006187	13609.0	80.197590	100.963847	7.047101	78.771548	0.006099	13511.666667	80.775305	101.691156	7.097866	79.338991	0.006143	15.408163	10.714286	0.382653	0.367347	0.000000	16.106667	11.200000	0.400000	0.384000	0.000000
42	2020-09-02	Central Massachusetts	1091759.0	1374767.0	95904.0	1076802.0	83.0	13705.0	99.137351	100.000000	99.947892	99.445882	100.000000	79.661364	100.311346	6.997738	78.570011	0.006056	13609.0	80.223308	101.018958	7.047101	79.124256	0.006099	13805.5	79.081453	99.581109	6.946797	77.998044	0.006012	13708.000000	79.643931	100.289393	6.996207	78.552816	0.006055	1.822917	3.906250	0.000000	25.000000	0.000000	1.804124	3.865979	0.000000	24.742268	0.000000
43	2020-09-09	Central Massachusetts	1101259.0	1374767.0	95954.0	1082802.0	83.0	13906.0	100.000000	100.000000	100.000000	100.000000	100.000000	79.193082	98.861427	6.900187	77.865813	0.005969	13805.5	79.769585	99.581109	6.950418	78.432654	0.006012	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	47.263682	0.000000	0.248756	29.850746	0.000000	48.346056	0.000000	0.254453	30.534351	0.000000